Angular mode simplification for geometry-based point cloud compression

ABSTRACT

A method of decoding point cloud data comprises obtaining a bitstream that includes an arithmetically encoded syntax element indicating a vertical plane position of a planar mode of a node; and decoding the vertical plane position of the planar mode in the node, wherein decoding the vertical plane position of the planar mode comprises: determining a laser index of a laser candidate in a set of laser candidates, wherein the determined laser index indicates a laser beam that intersects the node; determining a context index based on whether the laser beam is above a first distance threshold, between the first distance threshold and a second distance threshold, between the second distance threshold and a third distance threshold, or below the third distance threshold; and arithmetically decoding the vertical plane position of the planar mode using a context indicated by the determined context index.

This application claims the benefit of U.S. Provisional PatentApplication 63/007,282, filed Apr. 8, 2020, and U.S. Provisional PatentApplication 63/009,940, filed Apr. 14, 2020, the entire content of eachof which is incorporated by reference.

TECHNICAL FIELD

This disclosure relates to point cloud encoding and decoding.

BACKGROUND

A point cloud is a collection of points in a 3-dimensional space. Thepoints may correspond to points on objects within the 3-dimensionalspace. Thus, a point cloud may be used to represent the physical contentof the 3-dimensional space. Point clouds may have utility in a widevariety of situations. For example, point clouds may be used in thecontext of autonomous vehicles for representing the positions of objectson a roadway. In another example, point clouds may be used in thecontext of representing the physical content of an environment forpurposes of positioning virtual objects in an augmented reality (AR) ormixed reality (MR) application. Point cloud compression is a process forencoding and decoding point clouds. Encoding point clouds may reduce theamount of data required for storage and transmission of point clouds.

SUMMARY

Aspects of this disclosure describes techniques for encoding and/ordecoding angular modes bitstreams, such as in bitstreams carrying pointcloud data employing Geometry-based Point Cloud Compression (G-PCC). Asdescribed herein, syntax elements related to the angular mode, such assyntax elements indicating a vertical plane position and syntax elementsindicating a vertical point position offset, are coded using arithmeticcoding. Conventional processes for determining contexts for use inarithmetic coding of such syntax elements are complex. This disclosuredescribes techniques that may reduce the complexity of determiningcontexts for use in arithmetic coding of such syntax elements.

In one example, this disclosure describes a method of decoding pointcloud data, the method comprising: obtaining a geometry bitstream thatincludes an arithmetically encoded syntax element indicating a verticalplane position of a planar mode of a node of a tree that represents3-dimensional positions of points in a point cloud represented by thepoint cloud data; and decoding the vertical plane position of the planarmode in the node, wherein decoding the vertical plane position of theplanar mode comprises: determining a laser index of a laser candidate ina set of laser candidates, wherein the determined laser index indicatesa laser beam that intersects the node; determining a context index basedon whether the laser beam is above a first distance threshold, betweenthe first distance threshold and a second distance threshold, betweenthe second distance threshold and a third distance threshold, or belowthe third distance threshold; and arithmetically decoding the verticalplane position of the planar mode using a context indicated by thedetermined context index.

In another example, this disclosure describes a method of encoding pointcloud data, the method comprising: encoding a vertical plane position ofa planar mode in a node of a tree that represents 3-dimensionalpositions of points in a point cloud represented by the point clouddata, wherein encoding the vertical plane position of the planar modecomprises: determining a laser index of a laser candidate in a set oflaser candidates, wherein the determined laser index indicates a laserbeam that intersects the node; determining a context index based onwhether the laser beam is above a first distance threshold, between thefirst distance threshold and a second distance threshold, between thesecond distance threshold and a third distance threshold, or below thethird distance threshold; and arithmetically encoding the vertical planeposition of the planar mode using a context indicated by the determinedcontext index.

In another example, this disclosure describes a device for decodingpoint cloud data, the device comprising: a memory to store the pointcloud data; and one or more processors coupled to the memory andimplemented in circuitry, the one or more processors configured to:obtain a geometry bitstream that includes an arithmetically encodedsyntax element indicating a vertical plane position of a planar mode ofa node of a tree that represents 3-dimensional positions of points in apoint cloud represented by the point cloud data; and decode the verticalplane position of the planar mode in the node, wherein the one or moreprocessors are configured such that, as part of decoding the verticalplane position of the planar mode, the one or more processors: determinea laser index of a laser candidate in a set of laser candidates, whereinthe determined laser index indicates a laser beam that intersects thenode; determine a context index based on whether the laser beam is abovea first distance threshold, between the first distance threshold and asecond distance threshold, between the second distance threshold and athird distance threshold, or below the third distance threshold; andarithmetically decode the vertical plane position of the planar modeusing a context indicated by the determined context index.

In another example, this disclosure describes a device for encodingpoint cloud data, the device comprising: a memory to store the pointcloud data; and one or more processors coupled to the memory andimplemented in circuitry, the one or more processors configured toencode a point cloud represented by the point cloud data, wherein theone or more processors are configured to, as part of encoding the pointcloud: encode a vertical plane position of a planar mode in a node of atree that represents 3-dimensional positions of points in the pointcloud, wherein the one or more processors are configured such that, aspart of encoding the vertical plane position of the planar mode, the oneor more processors: determine a laser index of a laser candidate in aset of laser candidates, wherein the determined laser index indicates alaser beam that intersects the node; determine a context index based onwhether the laser beam is above a first distance threshold, between thefirst distance threshold and a second distance threshold, between thesecond distance threshold and a third distance threshold, or below thethird distance threshold; and arithmetically encode the vertical planeposition of the planar mode using a context indicated by the determinedcontext index.

In another example, this disclosure describes a device for decodingpoint cloud data, the device comprising: means for obtaining a geometrybitstream that includes an arithmetically encoded syntax elementindicating a vertical plane position of a planar mode of a node of atree that represents 3-dimensional positions of points in a point cloudrepresented by the point cloud data; and means for decoding the verticalplane position of the planar mode in the node, wherein the means fordecoding the vertical plane position of the planar mode comprises: meansfor determining a laser index of a laser candidate in a set of lasercandidates, wherein the determined laser index indicates a laser beamthat intersects the node; means for determining a context index based onwhether the laser beam is above a first distance threshold, between thefirst distance threshold and a second distance threshold, between thesecond distance threshold and a third distance threshold, or below thethird distance threshold; and means for arithmetically decoding thevertical plane position of the planar mode using a context indicated bythe determined context index.

In another example, this disclosure describes a device for encodingpoint cloud data, the device comprising: means for encoding the pointcloud data, wherein the means for encoding the point cloud datacomprises means for encoding a vertical plane position of a planar modein a node of a tree that represents 3-dimensional positions of points ina point cloud represented by the point cloud data, wherein the means forencoding the vertical plane position of the planar mode comprises: meansfor determining a laser index of a laser candidate in a set of lasercandidates, wherein the determined laser index indicates a laser beamthat intersects the node; means for determining a context index based onwhether the laser beam is above a first distance threshold, between thefirst distance threshold and a second distance threshold, between thesecond distance threshold and a third distance threshold, or below thethird distance threshold; and means for arithmetically encoding thevertical plane position of the planar mode using a context indicated bythe determined context index.

In another example, this disclosure describes a computer-readablestorage medium having stored thereon instructions that, when executed,cause one or more processors to: obtain a geometry bitstream thatincludes an arithmetically encoded syntax element indicating a verticalplane position of a planar mode of a node of a tree that represents3-dimensional positions of points in a point cloud; and decode thevertical plane position of the planar mode in the node, wherein theinstructions that cause the one or more processors to decode thevertical plane position of the planar mode comprise instructions that,when executed, cause the one or more processors to: determine a laserindex of a laser candidate in a set of laser candidates, wherein thedetermined laser index indicates a laser beam that intersects the node;determine a context index based on whether the laser beam is above afirst distance threshold, between the first distance threshold and asecond distance threshold, between the second distance threshold and athird distance threshold, or below the third distance threshold; andarithmetically decode the vertical plane position of the planar modeusing a context indicated by the determined context index.

In another example, this disclosure describes a computer-readablestorage medium having stored thereon instructions that, when executed,cause one or more processors to: encode a point cloud, wherein theinstructions that cause the one or more processors to encode the pointcloud comprises instructions that, when executed, cause the one or moreprocessors to encode a vertical plane position of a planar mode in anode of a tree that represents 3-dimensional positions of points in thepoint cloud, wherein the instructions that cause the one or moreprocessors to encode the vertical plane position of the planar modecomprise instructions that, when executed, cause the one or moreprocessors to: determine a laser index of a laser candidate in a set oflaser candidates, wherein the determined laser index indicates a laserbeam that intersects the node; determine a context index based onwhether the laser beam is above a first distance threshold, between thefirst distance threshold and a second distance threshold, between thesecond distance threshold and a third distance threshold, or below thethird distance threshold; and arithmetically encode the vertical planeposition of the planar mode using a context indicated by the determinedcontext index.

In another example, this disclosure describes a method of decoding pointcloud data, the method comprising: obtaining a geometry bitstream thatincludes an arithmetically encoded syntax element indicating a verticalpoint position offset within a node of a tree that represents3-dimensional positions of points in a point cloud represented by thepoint cloud data; and decoding the vertical point position offset,wherein decoding the vertical point position offset comprises:determining a laser index of a laser candidate in a set of lasercandidates, wherein the determined laser index indicates a laser beamthat intersects the node; determining a context index based on whetherthe laser beam is above a first distance threshold, between the firstdistance threshold and a second distance threshold, between the seconddistance threshold and a third distance threshold, or below the thirddistance threshold; and arithmetically decoding a bin of the verticalpoint position offset using a context indicated by the determinedcontext index.

In another example, this disclosure describes a method of encoding pointcloud data, the method comprising: encoding a vertical point positionoffset within a node of a tree that represents 3-dimensional positionsof points in a point cloud represented by the point cloud data, whereinencoding the vertical point position offset comprises: determining alaser index of a laser candidate in a set of laser candidates, whereinthe determined laser index indicates a laser beam that intersects thenode; determining a context index based on whether the laser beam isabove a first distance threshold, between the first distance thresholdand a second distance threshold, between the second distance thresholdand a third distance threshold, or below the third distance threshold;and arithmetically encoding a bin of the vertical point position offsetusing a context indicated by the determined context index.

In another example, this disclosure describes a device for decodingpoint cloud data, the device comprising: a memory to store the pointcloud data; and one or more processors coupled to the memory andimplemented in circuitry, the one or more processors configured to:obtain a geometry bitstream that includes an arithmetically encodedsyntax element indicating a vertical point position offset within a nodeof a tree that represents 3-dimensional positions of points in a pointcloud represented by the point cloud data; and decode the vertical pointposition offset, wherein the one or more processors are configured suchthat, as part of decoding the vertical point position offset, the one ormore processors: determine a laser index of a laser candidate in a setof laser candidates, wherein the determined laser index indicates alaser beam that intersects the node; determine a context index based onwhether the laser beam is above a first distance threshold, between thefirst distance threshold and a second distance threshold, between thesecond distance threshold and a third distance threshold, or below thethird distance threshold; and arithmetically decode a bin of thevertical point position offset using a context indicated by thedetermined context index.

In another example, this disclosure describes a device for encodingpoint cloud data, the device comprising: a memory to store the pointcloud data; and one or more processors coupled to the memory andimplemented in circuitry, the one or more processors configured to:encode a vertical point position offset within a node of a tree thatrepresents 3-dimensional positions of points in a point cloudrepresented by the point cloud data, wherein the one or more processorsare configured to, as part of encoding the vertical point positionoffset: determine a laser index of a laser candidate in a set of lasercandidates, wherein the determined laser index indicates a laser beamthat intersects the node; determine a context index based on whether thelaser beam is above a first distance threshold, between the firstdistance threshold and a second distance threshold, between the seconddistance threshold and a third distance threshold, or below the thirddistance threshold; and arithmetically encode a bin of the verticalpoint position offset using a context indicated by the determinedcontext index.

In another example, this disclosure describes a device for decodingpoint cloud data, the device comprising: means for obtaining a geometrybitstream that includes an arithmetically encoded syntax elementindicating a vertical point position offset within a node of a tree thatrepresents 3-dimensional positions of points in a point cloudrepresented by the point cloud data; and means for decoding the verticalpoint position offset, wherein the means for decoding the vertical pointposition offset comprises: means for determining a laser index of alaser candidate in a set of laser candidates, wherein the determinedlaser index indicates a laser beam that intersects the node; means fordetermining a context index based on whether the laser beam is above afirst distance threshold, between the first distance threshold and asecond distance threshold, between the second distance threshold and athird distance threshold, or below the third distance threshold; andmeans for arithmetically decoding a bin of the vertical point positionoffset using a context indicated by the determined context index.

In another example, this disclosure describes a device for encodingpoint cloud data, the device comprising: means for encoding a verticalpoint position offset within a node of a tree that represents3-dimensional positions of points in a point cloud represented by thepoint cloud data, wherein the means for encoding the vertical pointposition offset comprises: means for determining a laser index of alaser candidate in a set of laser candidates, wherein the determinedlaser index indicates a laser beam that intersects the node; means fordetermining a context index based on whether the laser beam is above afirst distance threshold, between the first distance threshold and asecond distance threshold, between the second distance threshold and athird distance threshold, or below the third distance threshold; andmeans for arithmetically encoding a bin of the vertical point positionoffset using a context indicated by the determined context index.

In another example, this disclosure describes a computer-readablestorage medium having stored thereon instructions that, when executed,cause one or more processors to: obtain a geometry bitstream thatincludes an arithmetically encoded syntax element indicating a verticalpoint position offset within a node of a tree that represents3-dimensional positions of points in a point cloud; and decode thevertical point position offset, wherein the instructions that cause theone or more processors to decode the vertical point position offsetcomprise instructions that, when executed, cause the one or moreprocessors to: determine a laser index of a laser candidate in a set oflaser candidates, wherein the determined laser index indicates a laserbeam that intersects the node; determine a context index based onwhether the laser beam is above a first distance threshold, between thefirst distance threshold and a second distance threshold, between thesecond distance threshold and a third distance threshold, or below thethird distance threshold; and arithmetically decode a bin of thevertical point position offset using a context indicated by thedetermined context index.

In another example, this disclosure describes a computer-readablestorage medium having stored thereon instructions that, when executed,cause one or more processors to: encode a vertical point position offsetwithin a node of a tree that represents 3-dimensional positions ofpoints in a point cloud, wherein the instructions that cause the one ormore processors to encode the vertical point position offset compriseinstructions that, when executed, cause the one or more processors to:determine a laser index of a laser candidate in a set of lasercandidates, wherein the determined laser index indicates a laser beamthat intersects the node; determine a context index based on whether thelaser beam is above a first distance threshold, between the firstdistance threshold and a second distance threshold, between the seconddistance threshold and a third distance threshold, or below the thirddistance threshold; and arithmetically encode a bin of the verticalpoint position offset using a context indicated by the determinedcontext index.

The details of one or more examples are set forth in the accompanyingdrawings and the description below. Other features, objects, andadvantages will be apparent from the description, drawings, and claims.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram illustrating an example encoding and decodingsystem that may perform the techniques of this disclosure.

FIG. 2 is a block diagram illustrating an example Geometry Point CloudCompression (G-PCC) encoder.

FIG. 3 is a block diagram illustrating an example G-PCC decoder.

FIG. 4 is a conceptual diagram illustrating example planar occupancy ina vertical direction.

FIG. 5 is a conceptual diagram of an example in which a context index isdetermined based on laser beam positions above or below a marker pointof a node, in accordance with one or more techniques of this disclosure.

FIG. 6 is a conceptual diagram illustrating an example three-contextindex determination.

FIG. 7 is a conceptual diagram illustrating an example context indexdetermination for coding a planar mode's vertical plane position basedon a laser beam position with intervals separated by finely dottedlines.

FIG. 8A is a flowchart illustrating an example operation for encoding avertical plane position in accordance with one or more techniques ofthis disclosure.

FIG. 8B is a flowchart illustrating an example operation for decoding avertical plane position in accordance with one or more techniques ofthis disclosure.

FIG. 9 is a conceptual diagram of an example for determining a contextindex based on laser beam positions above or below the intervalmid-point.

FIG. 10 is a conceptual diagram illustrating an example Inferred DirectCoding Mode (IDCM) vertical point offset interval corresponding withj-th bit divided into three subintervals.

FIG. 11 is a conceptual diagram illustrating an example technique fordetermining a context index for coding IDCM's vertical point positionoffsets based on a laser beam position within intervals.

FIG. 12A is a flowchart illustrating an example operation for encodingan IDCM vertical point offset in accordance with one or more techniquesof this disclosure.

FIG. 12B is a flowchart illustrating an example operation for decodingan IDCM vertical point offset in accordance with one or more techniquesof this disclosure.

FIG. 13 is a conceptual diagram indicating corners and sides of a righttriangle.

FIG. 14 is a conceptual diagram illustrating an example range-findingsystem that may be used with one or more techniques of this disclosure.

FIG. 15 is a conceptual diagram illustrating an example vehicle-basedscenario in which one or more techniques of this disclosure may be used.

FIG. 16 is a conceptual diagram illustrating an example extended realitysystem in which one or more techniques of this disclosure may be used.

FIG. 17 is a conceptual diagram illustrating an example mobile devicesystem in which one or more techniques of this disclosure may be used.

DETAILED DESCRIPTION

A point cloud is a collection of points in a 3-dimensional (3D) space.Point cloud data may include all or some data representing a pointcloud. Geometry-based point cloud compression (G-PCC) is an approach forreducing the amount of data needed to encode or store point clouds. Aspart of encoding a point cloud, a G-PCC encoder generates an octree.Each node of the octree corresponds to a cuboid space. For ease ofexplanation, this disclosure may, in some circumstances, refer to a nodeand the cuboid space corresponding to the node interchangeably. Nodes ofthe octree can have zero child nodes or eight child nodes. In otherexamples, nodes can be divided into child nodes according to other treestructures. The child nodes of a parent correspond to equally sizedcuboids within the cuboid corresponding to the parent node. Thepositions of individual points of a point cloud can be signaled relativeto nodes corresponding to cuboids containing the points. If a cuboidcorresponding to a node does not contain any points of the point cloud,the node is said to be unoccupied. If the node is unoccupied, it may notbe necessary to signal additional data with respect to the node.Conversely, if a cuboid corresponding to a node contains one or morepoints of the point cloud, the node is said to be occupied.

Planar mode is a technique that may improve encoding or signaling ofwhich nodes in the octree are occupied. Planar mode may be used when alloccupied child nodes of the node are adjacent to a plane and on a sideof the plane associated with increasing coordinate values for adimension orthogonal to the plane. For instance, planar mode may be usedfor a node when all occupied child nodes of the node are above or belowa horizontal plane passing through a center point of the node, or planarmode may be used for a node when all occupied child nodes of the nodeare on a close side or a farther side of a vertical plane passingthrough the center point of the node. A G-PCC encoder may signal a planeposition syntax element for each of an x, y, and z dimension. The planeposition syntax element for a dimension (e.g., an x, y, or z dimension)indicates whether the plane orthogonal to the dimension is at a firstposition or a second position. If the plane is at the first position,the plane corresponds to a boundary of the node. If the plane is at thesecond position, the plane passes through a 3D center of the node. Thus,for the z-dimension, a G-PCC encoder or G-PCC decoder may code avertical plane position of a planar mode in a node of an octree thatrepresents 3-dimensional positions of points in the point cloud.

The G-PCC coder (e.g., a G-PCC encoder or G-PCC decoder) may usearithmetic coding to code plane position syntax elements. When the G-PCCcoder uses arithmetic coding to code a plane position syntax element,the G-PCC coder determines a context index that indicates a context touse for arithmetic coding of the plane position syntax element. Acontext specifies probabilities for symbols used in arithmetic coding.As described in greater detail elsewhere in this disclosure,conventional techniques for determining the context index are complex.Such complexity may slow the process of coding the point cloud.Moreover, such complexity may increase the cost of hardware that may beused to implement encoders and decoders.

This disclosure describes techniques that may reduce the complexity ofdetermining the context index. For instance, a G-PCC coder may code avertical plane position of a planar mode in a node of an octree thatrepresents 3-dimensional positions of points in the point cloud. As partof coding the vertical plane position of the planar mode, the G-PCCcoder may determine a laser index of a laser candidate in a set of lasercandidates. The determined laser index indicates a laser beam thatintersects the node. Additionally, the G-PCC coder may determine acontext index based on an intersection of the laser beam and the node.For instance, the G-PCC coder may determine a context index based onwhether the laser beam is above a first distance threshold, between thefirst distance threshold and a second distance threshold, between thesecond distance threshold and a third distance threshold, or below thethird distance threshold. The G-PCC coder may arithmetically code thevertical plane position of the planar mode using a context indicated bythe determined context index. Determining the context index based on theintersection of the beam and the node in this way may reduce complexityof determining the context index. Although this disclosure describeslasers, laser beams, laser candidates, and other terms involving lasers,these terms are not necessarily limited to instances in which physicallasers are used. Rather, these terms may be used with respect tophysical lasers or other range-finding technologies. Moreover, theseterms may be used with respect to conceptual beams used for purposes ofcoding point clouds. In other words, the terms “laser,” “laser beam,”etc., may not refer to real lasers and laser beams, but rather theconcept of a laser and laser beam may be used for purposes of codingpoint clouds.

As noted above, the positions of individual points of a point cloud canbe encoded relative to nodes containing the points. In some examples,the positions of points in a node may be encoded using an inferreddirect coding mode (IDCM). When a point is signaled using IDCM, a G-PCCencoder encodes a point offset that indicates an offset, in a specificdimension (e.g., a vertical dimension, horizontal dimension, lateraldimension, etc.), of the point relative to an origin point of the node.A point offset may be referred to as a point position offset. G-PCCcoders may determine a context and use the context in arithmetic codingof the point offset. Conventional techniques for determining the contextto use in arithmetic coding of a point offset have been complex.

This disclosure describes techniques that may reduce the complexity ofprocesses for determining a context to use for arithmetic coding ofpoint offsets. For instance, as described in this disclosure, a G-PCCcoder may code a vertical point position offset within a node of a tree(e.g., an octree) that represents 3-dimensional positions of points inthe point cloud. As part of coding the vertical point position offset,the G-PCC coder may determine a laser index of a laser candidate in aset of laser candidates. The determined laser index indicates a laserbeam that intersects the node. Additionally, the G-PCC coder maydetermine a context index based on whether the laser beam is above afirst distance threshold, between the first distance threshold and asecond distance threshold, between the second distance threshold and athird distance threshold, or below the third distance threshold. TheG-PCC coder may arithmetically code bins of the vertical point positionoffset using a context indicated by the determined context index.

FIG. 1 is a block diagram illustrating an example encoding and decodingsystem 100 that may perform the techniques of this disclosure. Thetechniques of this disclosure are generally directed to coding (encodingand/or decoding) point cloud data, i.e., to support point cloudcompression. In general, point cloud data includes any data forprocessing a point cloud. The coding may be effective in compressingand/or decompressing point cloud data.

As shown in FIG. 1 , system 100 includes a source device 102 and adestination device 116. Source device 102 provides encoded point clouddata to be decoded by a destination device 116. Particularly, in theexample of FIG. 1 , source device 102 provides the point cloud data todestination device 116 via a computer-readable medium 110. Source device102 and destination device 116 may comprise any of a wide range ofdevices, including desktop computers, notebook (i.e., laptop) computers,tablet computers, set-top boxes, telephone handsets such as smartphones,televisions, cameras, display devices, digital media players, videogaming consoles, video streaming devices, terrestrial or marinevehicles, spacecraft, aircraft, robots, LIDAR devices, satellites,surveillance or security equipment, or the like. In some cases, sourcedevice 102 and destination device 116 may be equipped for wirelesscommunication.

In the example of FIG. 1 , source device 102 includes a data source 104,a memory 106, a G-PCC encoder 200, and an output interface 108.Destination device 116 includes an input interface 122, a G-PCC decoder300, a memory 120, and a data consumer 118. G-PCC encoder 200 of sourcedevice 102 and G-PCC decoder 300 of destination device 116 may beconfigured to apply the techniques of this disclosure related tosimplifications for G-PCC angular modes. Thus, source device 102represents an example of an encoding device, while destination device116 represents an example of a decoding device. In other examples,source device 102 and destination device 116 may include othercomponents or arrangements. For example, source device 102 may receivedata (e.g., point cloud data) from an internal or external source.Likewise, destination device 116 may interface with an external dataconsumer, rather than include a data consumer in the same device.

System 100 as shown in FIG. 1 is merely one example. In general, otherdigital encoding and/or decoding devices may perform the techniques ofthis disclosure related to simplifications for G-PCC angular modes.Source device 102 and destination device 116 are merely examples of suchdevices in which source device 102 generates coded data for transmissionto destination device 116. This disclosure refers to a “coding” deviceas a device that performs coding (encoding and/or decoding) of data.Thus, G-PCC encoder 200 and G-PCC decoder 300 represent examples ofcoding devices, in particular, an encoder and a decoder, respectively.Similarly, the term “coding” may refer to either of encoding ordecoding. In some examples, source device 102 and destination device 116may operate in a substantially symmetrical manner such that each ofsource device 102 and destination device 116 includes encoding anddecoding components. Hence, system 100 may support one-way or two-waytransmission between source device 102 and destination device 116, e.g.,for streaming, playback, broadcasting, telephony, navigation, and otherapplications.

In general, data source 104 represents a source of data (i.e., raw,unencoded point cloud data) and may provide a sequential series of“frames”) of the data to G-PCC encoder 200, which encodes data for theframes. Data source 104 of source device 102 may include a point cloudcapture device, such as any of a variety of sensors, e.g., a 3D scanner,a light detection and ranging (LIDAR) device, one or more image or videocameras, an archive containing previously captured data, and/or a datafeed interface to receive data from a data content provider. In thisway, data source 104 may generate a point cloud. Alternatively oradditionally, point cloud data may be computer-generated from scanner,camera, sensor or other data. For example, data source 104 may generatecomputer graphics-based data as the source data, or produce acombination of live data, archived data, and computer-generated data. Ineach case, G-PCC encoder 200 encodes the captured, pre-captured, orcomputer-generated data. G-PCC encoder 200 may rearrange the frames fromthe received order (sometimes referred to as “display order”) into acoding order for coding. G-PCC encoder 200 may generate one or morebitstreams including encoded data. Source device 102 may then output theencoded data via output interface 108 onto computer-readable medium 110for reception and/or retrieval by, e.g., input interface 122 ofdestination device 116.

Memory 106 of source device 102 and memory 120 of destination device 116may represent general purpose memories. In some examples, memory 106 andmemory 120 may store raw data, e.g., raw data from data source 104 andraw, decoded data from G-PCC decoder 300. Additionally or alternatively,memory 106 and memory 120 may store software instructions executable by,e.g., G-PCC encoder 200 and G-PCC decoder 300, respectively. Althoughmemory 106 and memory 120 are shown separately from G-PCC encoder 200and G-PCC decoder 300 in this example, it should be understood thatG-PCC encoder 200 and G-PCC decoder 300 may also include internalmemories for functionally similar or equivalent purposes. Furthermore,memory 106 and memory 120 may store encoded data, e.g., output fromG-PCC encoder 200 and input to G-PCC decoder 300. In some examples,portions of memory 106 and memory 120 may be allocated as one or morebuffers, e.g., to store raw, decoded, and/or encoded data. For instance,memory 106 and memory 120 may store data representing a point cloud.

Computer-readable medium 110 may represent any type of medium or devicecapable of transporting the encoded data from source device 102 todestination device 116. In one example, computer-readable medium 110represents a communication medium to enable source device 102 totransmit encoded data (e.g., an encoded point cloud) directly todestination device 116 in real-time, e.g., via a radio frequency networkor computer-based network. Output interface 108 may modulate atransmission signal including the encoded data, and input interface 122may demodulate the received transmission signal, according to acommunication standard, such as a wireless communication protocol. Thecommunication medium may comprise any wireless or wired communicationmedium, such as a radio frequency (RF) spectrum or one or more physicaltransmission lines. The communication medium may form part of apacket-based network, such as a local area network, a wide-area network,or a global network such as the Internet. The communication medium mayinclude routers, switches, base stations, or any other equipment thatmay be useful to facilitate communication from source device 102 todestination device 116.

In some examples, source device 102 may output encoded data from outputinterface 108 to storage device 112. Similarly, destination device 116may access encoded data from storage device 112 via input interface 122.Storage device 112 may include any of a variety of distributed orlocally accessed data storage media such as a hard drive, Blu-ray discs,DVDs, CD-ROMs, flash memory, volatile or non-volatile memory, or anyother suitable digital storage media for storing encoded data.

In some examples, source device 102 may output encoded data to fileserver 114 or another intermediate storage device that may store theencoded data generated by source device 102. Destination device 116 mayaccess stored data from file server 114 via streaming or download. Fileserver 114 may be any type of server device capable of storing encodeddata and transmitting that encoded data to the destination device 116.File server 114 may represent a web server (e.g., for a web site), aFile Transfer Protocol (FTP) server, a content delivery network device,or a network attached storage (NAS) device. Destination device 116 mayaccess encoded data from file server 114 through any standard dataconnection, including an Internet connection. This may include awireless channel (e.g., a Wi-Fi connection), a wired connection (e.g.,digital subscriber line (DSL), cable modem, etc.), or a combination ofboth that is suitable for accessing encoded data stored on file server114. File server 114 and input interface 122 may be configured tooperate according to a streaming transmission protocol, a downloadtransmission protocol, or a combination thereof.

Output interface 108 and input interface 122 may represent wirelesstransmitters/receivers, modems, wired networking components (e.g.,Ethernet cards), wireless communication components that operateaccording to any of a variety of IEEE 802.11 standards, or otherphysical components. In examples where output interface 108 and inputinterface 122 comprise wireless components, output interface 108 andinput interface 122 may be configured to transfer data, such as encodeddata, according to a cellular communication standard, such as 4G, 4G-LTE(Long-Term Evolution), LTE Advanced, 5G, or the like. In some exampleswhere output interface 108 comprises a wireless transmitter, outputinterface 108 and input interface 122 may be configured to transferdata, such as encoded data, according to other wireless standards, suchas an IEEE 802.11 specification, an IEEE 802.15 specification (e.g.,ZigBee™), a Bluetooth™ standard, or the like. In some examples, sourcedevice 102 and/or destination device 116 may include respectivesystem-on-a-chip (SoC) devices. For example, source device 102 mayinclude an SoC device to perform the functionality attributed to G-PCCencoder 200 and/or output interface 108, and destination device 116 mayinclude an SoC device to perform the functionality attributed to G-PCCdecoder 300 and/or input interface 122.

The techniques of this disclosure may be applied to encoding anddecoding in support of any of a variety of applications, such ascommunication between autonomous vehicles, communication betweenscanners, cameras, sensors and processing devices such as local orremote servers, geographic mapping, or other applications.

In some examples, source device 102 and/or destination device 116 aremobile devices, such as mobile phones, augmented reality (AR) devices,or mixed reality (MR) devices. In such examples, source device 102 maygenerate and encode a point cloud as part of a process to map the localenvironment of source device 102. With respect to AR and MR examples,destination device 116 may use the point cloud to generate a virtualenvironmental based on the local environment of source device 102. Insome examples, source device 102 and/or destination device 116 areterrestrial or marine vehicles, spacecraft, or aircraft. In suchexamples, source device 102 may generate and encode a point cloud aspart of a process to map an environment of source device, e.g., forpurposes of autonomous navigation, crash forensics, and other purposes.

Input interface 122 of destination device 116 receives an encodedbitstream from computer-readable medium 110 (e.g., a communicationmedium, storage device 112, file server 114, or the like). The encodedbitstream may include signaling information defined by G-PCC encoder200, which is also used by G-PCC decoder 300, such as syntax elementshaving values that describe characteristics and/or processing of codedunits (e.g., slices, pictures, groups of pictures, sequences, or thelike). Data consumer 118 uses the decoded data. For example, dataconsumer 118 may use the decoded data to determine the locations ofphysical objects. In some examples, data consumer 118 may comprise adisplay to present imagery based on a point cloud. For example, dataconsumer 118 may use points of the point cloud as vertices of polygonsand may use color attributes of points of the point cloud to shade thepolygons. In this example, data consumer 118 may then rasterize thepolygons to present computer-generated images based on the shadedpolygons.

G-PCC encoder 200 and G-PCC decoder 300 each may be implemented as anyof a variety of suitable encoder and/or decoder circuitry, such as oneor more microprocessors, digital signal processors (DSPs), applicationspecific integrated circuits (ASICs), field programmable gate arrays(FPGAs), discrete logic, software, hardware, firmware or anycombinations thereof. When the techniques are implemented partially insoftware, a device may store instructions for the software in asuitable, non-transitory computer-readable medium and execute theinstructions in hardware using one or more processors to perform thetechniques of this disclosure. Each of G-PCC encoder 200 and G-PCCdecoder 300 may be included in one or more encoders or decoders, eitherof which may be integrated as part of a combined encoder/decoder (CODEC)in a respective device. A device including G-PCC encoder 200 and/orG-PCC decoder 300 may comprise one or more integrated circuits,microprocessors, and/or other types of devices.

G-PCC encoder 200 and G-PCC decoder 300 may operate according to acoding standard, such as video point cloud compression (V-PCC) standardor a geometry point cloud compression (G-PCC) standard. This disclosuremay generally refer to coding (e.g., encoding and decoding) of picturesto include the process of encoding or decoding data. An encodedbitstream generally includes a series of values for syntax elementsrepresentative of coding decisions (e.g., coding modes).

This disclosure may generally refer to “signaling” certain information,such as syntax elements. The term “signaling” may generally refer to thecommunication of values for syntax elements and/or other data used todecode encoded data. That is, G-PCC encoder 200 may signal values forsyntax elements in the bitstream. In general, signaling refers togenerating a value in the bitstream. As noted above, source device 102may transport the bitstream to destination device 116 substantially inreal time, or not in real time, such as might occur when storing syntaxelements to storage device 112 for later retrieval by destination device116.

Point cloud compression activities are categorized in two differentapproaches. The first approach is “Video point cloud compression”(V-PCC), which segments the 3D object, and projects the segments inmultiple 2D planes (which are represented as “patches” in the 2D frame),which are further coded by a video codec such as a High Efficiency VideoCoding (HEVC) (ITU-T H.265) codec. The second approach is“Geometry-based point cloud compression” (G-PCC), which directlycompresses 3D geometry, i.e., position of a set of points in 3D space,and associated attribute values (for each point associated with the 3Dgeometry). G-PCC addresses the compression of point clouds in bothCategory 1 (static point clouds) and Category 3 (dynamically acquiredpoint clouds). A recent draft of the G-PCC standard is available inG-PCC DIS, ISO/IEC JTC1/SC29/WG11 w19088, Brussels, Belgium, January2020 (hereinafter, “w19088”), and a description of the codec isavailable in G-PCC Codec Description v6, ISO/IEC JTC1/SC29/WG11 w19091,Brussels, Belgium, January 2020 (hereinafter, “w19091”).

A point cloud contains a set of points in a 3D space and may haveattributes associated with the points. The attributes may be colorinformation such as R, G, B or Y, Cb, Cr, or reflectance information, orother attributes. Point clouds may be captured by a variety of camerasor sensors such as LIDAR sensors and 3D scanners and may also becomputer-generated. Point cloud data are used in a variety ofapplications including, but not limited to, construction (modeling),graphics (3D models for visualizing and animation), the automotiveindustry (LIDAR sensors used to help in navigation), and in otherapplications that may employ the use of mobile phones, tablet computers,or other computing devices.

The 3D space occupied by a point cloud data may be enclosed by a virtualbounding box. The position of the points in the bounding box may berepresented by a certain precision; therefore, the positions of one ormore points may be quantized based on the precision. At the smallestlevel, the bounding box is split into voxels which are the smallest unitof space represented by a unit cube. A voxel in the bounding box may beassociated with zero, one, or more than one point. The bounding box maybe split into multiple cube/cuboid regions, which may be called tiles.Each tile may be coded into one or more slices. The partitioning of thebounding box into slices and tiles may be based on number of points ineach partition, or based on other considerations (e.g., a particularregion may be coded as tiles). The slice regions may be furtherpartitioned using splitting decisions similar to those in video codecs.

FIG. 2 provides an overview of G-PCC encoder 200. FIG. 3 provides anoverview of G-PCC decoder 300. The modules shown are logical, and do notnecessarily correspond one-to-one to implemented code in the referenceimplementation of G-PCC codec, i.e., TMC13 test model software studiedby ISO/IEC MPEG (JTC 1/SC 29/WG 11).

In both G-PCC encoder 200 and G-PCC decoder 300, point cloud positionsare coded first. Attribute coding depends on the decoded geometry. InFIG. 2 and FIG. 3 , the gray-shaded modules are options typically usedfor Category 1 data. Diagonal-crosshatched modules are options typicallyused for Category 3 data. All the other modules are common betweenCategories 1 and 3.

For Category 3 data, the compressed geometry is typically represented asan octree from the root all the way down to a leaf level of individualvoxels. For Category 1 data, the compressed geometry is typicallyrepresented by a pruned octree (i.e., an octree from the root down to aleaf level of blocks larger than voxels) plus a model that approximatesthe surface within each leaf of the pruned octree. In this way, bothCategory 1 and 3 data share the octree coding mechanism, while Category1 data may in addition approximate the voxels within each leaf with asurface model. The surface model used is a triangulation comprising 1-10triangles per block, resulting in a triangle soup. The Category 1geometry codec is therefore known as the Trisoup geometry codec, whilethe Category 3 geometry codec is known as the Octree geometry codec.

At each node of an octree, an occupancy is signaled (when not inferred)for one or more of its child nodes (up to eight nodes). Multipleneighborhoods are specified including (a) nodes that share a face with acurrent octree node, (b) nodes that share a face, edge or a vertex withthe current octree node, etc. Within each neighborhood, the occupancy ofa node and/or its children may be used to predict the occupancy of thecurrent node or its children. For points that are sparsely populated incertain nodes of the octree, the codec (e.g., as implemented by G-PCCencoder 200 and G-PCC decoder 300) also supports a direct coding modewhere the 3D position of the point is encoded directly. A flag may besignaled to indicate that a direct mode is signaled. At the lowestlevel, the number of points associated with the octree node/leaf nodemay also be coded.

Once the geometry is coded, the attributes corresponding to the geometrypoints are coded. When there are multiple attribute points correspondingto one reconstructed/decoded geometry point, an attribute value may bederived that is representative of the reconstructed point.

There are three attribute coding methods in G-PCC: Region AdaptiveHierarchical Transform (RAHT) coding, interpolation-based hierarchicalnearest-neighbour prediction (Predicting Transform), andinterpolation-based hierarchical nearest-neighbor prediction with anupdate/lifting step (Lifting Transform). RAHT and Lifting are typicallyused for Category 1 data, while Predicting is typically used forCategory 3 data. However, either method may be used for any data, and,similar with the geometry codecs in G-PCC, the attribute coding methodused to code the point cloud is specified in the bitstream.

The coding of the attributes may be conducted in a level-of-detail(LoD), where for with each level of detail, a finer representation ofthe point cloud attribute may be obtained. Each level of detail may bespecified based on a distance metric from the neighboring nodes or basedon a sampling distance.

At G-PCC encoder 200, the residuals obtained as the output of the codingmethods for the attributes are quantized. The quantized residuals may becoded using context adaptive arithmetic coding. To apply CABAC encodingto a syntax element, G-PCC encoder 200 may binarize the value of thesyntax element to form a series of one or more bits, which are referredto as “bins.” In addition, G-PCC encoder 200 may identify a codingcontext. The coding context may identify probabilities of bins havingparticular values. For instance, a coding context may indicate a 0.7probability of coding a 0-valued bin and a 0.3 probability of coding a1-valued bin. After identifying the coding context, G-PCC encoder 200may divide an interval into a lower sub-interval and an uppersub-interval. One of the sub-intervals may be associated with the value0 and the other sub-interval may be associated with the value 1. Thewidths of the sub-intervals may be proportional to the probabilitiesindicated for the associated values by the identified coding context. Ifa bin of the syntax element has the value associated with the lowersub-interval, the encoded value may be equal to the lower boundary ofthe lower sub-interval. If the same bin of the syntax element has thevalue associated with the upper sub-interval, the encoded value may beequal to the lower boundary of the upper sub-interval. To encode thenext bin of the syntax element, G-PCC encoder 200 may repeat these stepswith the interval being the sub-interval associated with the value ofthe encoded bit. When G-PCC encoder 200 repeats these steps for the nextbin, G-PCC encoder 200 may use modified probabilities based on theprobabilities indicated by the identified coding context and the actualvalues of bins encoded.

When G-PCC decoder 300 performs CABAC decoding on a value of a syntaxelement, G-PCC decoder 300 may identify a coding context. G-PCC decoder300 may then divide an interval into a lower sub-interval and an uppersub-interval. One of the sub-intervals may be associated with the value0 and the other sub-interval may be associated with the value 1. Thewidths of the sub-intervals may be proportional to the probabilitiesindicated for the associated values by the identified coding context. Ifthe encoded value is within the lower sub-interval, G-PCC decoder 300may decode a bin having the value associated with the lowersub-interval. If the encoded value is within the upper sub-interval,G-PCC decoder 300 may decode a bin having the value associated with theupper sub-interval. To decode a next bin of the syntax element, G-PCCdecoder 300 may repeat these steps with the interval being thesub-interval that contains the encoded value. When G-PCC decoder 300repeats these steps for the next bin, G-PCC decoder 300 may use modifiedprobabilities based on the probabilities indicated by the identifiedcoding context and the decoded bins. G-PCC decoder 300 may thende-binarize the bins to recover the value of the syntax element.

In the example of FIG. 2 , G-PCC encoder 200 may include a coordinatetransform unit 202, a color transform unit 204, a voxelization unit 206,an attribute transfer unit 208, an octree analysis unit 210, a surfaceapproximation analysis unit 212, an arithmetic encoding unit 214, ageometry reconstruction unit 216, an RAHT unit 218, a LOD generationunit 220, a lifting unit 222, a coefficient quantization unit 224, andan arithmetic encoding unit 226.

As shown in the example of FIG. 2 , G-PCC encoder 200 may receive a setof positions of points in the point cloud and a set of attributes. G-PCCencoder 200 may obtain the set of positions of the points in the pointcloud and the set of attributes from data source 104 (FIG. 1 ). Thepositions may include coordinates of points in a point cloud. Theattributes may include information about the points in the point cloud,such as colors associated with points in the point cloud. G-PCC encoder200 may generate a geometry bitstream 203 that includes an encodedrepresentation of the positions of the points in the point cloud. G-PCCencoder 200 may also generate an attribute bitstream 205 that includesan encoded representation of the set of attributes.

Coordinate transform unit 202 may apply a transform to the coordinatesof the points to transform the coordinates from an initial domain to atransform domain. This disclosure may refer to the transformedcoordinates as transform coordinates. Color transform unit 204 may applya transform in order to transform color information of the attributes toa different domain. For example, color transform unit 204 may transformcolor information from an RGB color space to a YCbCr color space.

Furthermore, in the example of FIG. 2 , voxelization unit 206 mayvoxelize the transform coordinates. Voxelization of the transformcoordinates may include quantization and removing some points of thepoint cloud. In other words, multiple points of the point cloud may besubsumed within a single “voxel,” which may thereafter be treated insome respects as one point. Furthermore, octree analysis unit 210 maygenerate an octree based on the voxelized transform coordinates.Additionally, in the example of FIG. 2 , surface approximation analysisunit 212 may analyze the points to potentially determine a surfacerepresentation of sets of the points. Arithmetic encoding unit 214 mayentropy encode syntax elements representing the information of theoctree and/or surfaces determined by surface approximation analysis unit212. G-PCC encoder 200 may output these syntax elements in geometrybitstream 203. Geometry bitstream 203 may also include other syntaxelements, including syntax elements that are not arithmetically encoded.

Geometry reconstruction unit 216 may reconstruct transform coordinatesof points in the point cloud based on the octree, data indicating thesurfaces determined by surface approximation analysis unit 212, and/orother information. The number of transform coordinates reconstructed bygeometry reconstruction unit 216 may be different from the originalnumber of points of the point cloud because of voxelization and surfaceapproximation. This disclosure may refer to the resulting points asreconstructed points. Attribute transfer unit 208 may transferattributes of the original points of the point cloud to reconstructedpoints of the point cloud.

Furthermore, RAHT unit 218 may apply RAHT coding to the attributes ofthe reconstructed points. Alternatively or additionally, LOD generationunit 220 and lifting unit 222 may apply LOD processing and lifting,respectively, to the attributes of the reconstructed points. RAHT unit218 and lifting unit 222 may generate coefficients based on theattributes. Coefficient quantization unit 224 may quantize thecoefficients generated by RAHT unit 218 or lifting unit 222. Arithmeticencoding unit 226 may apply arithmetic coding to syntax elementsrepresenting the quantized coefficients. G-PCC encoder 200 may outputthese syntax elements in attribute bitstream 205. Attribute bitstream205 may also include other syntax elements, including non-arithmeticallyencoded syntax elements.

In the example of FIG. 3 , G-PCC decoder 300 may include a geometryarithmetic decoding unit 302, an attribute arithmetic decoding unit 304,an octree synthesis unit 306, an inverse quantization unit 308, asurface approximation synthesis unit 310, a geometry reconstruction unit312, a RAHT unit 314, a LOD generation unit 316, an inverse lifting unit318, an inverse transform coordinate unit 320, and an inverse transformcolor unit 322.

G-PCC decoder 300 may obtain geometry bitstream 203 and attributebitstream 205. Geometry arithmetic decoding unit 302 of decoder 300 mayapply arithmetic decoding (e.g., Context-Adaptive Binary ArithmeticCoding (CABAC) or other type of arithmetic decoding) to syntax elementsin the geometry bitstream. Similarly, attribute arithmetic decoding unit304 may apply arithmetic decoding to syntax elements in the attributebitstream.

Octree synthesis unit 306 may synthesize an octree based on syntaxelements parsed from the geometry bitstream. In instances where surfaceapproximation is used in the geometry bitstream, surface approximationsynthesis unit 310 may determine a surface model based on syntaxelements parsed from the geometry bitstream and based on the octree.

Furthermore, geometry reconstruction unit 312 may perform areconstruction to determine coordinates of points in a point cloud.Inverse transform coordinate unit 320 may apply an inverse transform tothe reconstructed coordinates to convert the reconstructed coordinates(positions) of the points in the point cloud from a transform domainback into an initial domain.

Additionally, in the example of FIG. 3 , inverse quantization unit 308may inverse quantize attribute values. The attribute values may be basedon syntax elements obtained from the attribute bitstream (e.g.,including syntax elements decoded by attribute arithmetic decoding unit304).

Depending on how the attribute values are encoded, RAHT unit 314 mayperform RAHT coding to determine, based on the inverse quantizedattribute values, color values for points of the point cloud.Alternatively, LOD generation unit 316 and inverse lifting unit 318 maydetermine color values for points of the point cloud using a level ofdetail-based technique.

Furthermore, in the example of FIG. 3 , inverse transform color unit 322may apply an inverse color transform to the color values. The inversecolor transform may be an inverse of a color transform applied by colortransform unit 204 of encoder 200. For example, color transform unit 204may transform color information from an RGB color space to a YCbCr colorspace. Accordingly, inverse color transform unit 322 may transform colorinformation from the YCbCr color space to the RGB color space.

The various units of FIG. 2 and FIG. 3 are illustrated to assist withunderstanding the operations performed by encoder 200 and decoder 300.The units may be implemented as fixed-function circuits, programmablecircuits, or a combination thereof. Fixed-function circuits refer tocircuits that provide particular functionality, and are preset on theoperations that can be performed. Programmable circuits refer tocircuits that can be programmed to perform various tasks, and provideflexible functionality in the operations that can be performed. Forinstance, programmable circuits may execute software or firmware thatcause the programmable circuits to operate in the manner defined byinstructions of the software or firmware. Fixed-function circuits mayexecute software instructions (e.g., to receive parameters or outputparameters), but the types of operations that the fixed-functioncircuits perform are generally immutable. In some examples, one or moreof the units may be distinct circuit blocks (fixed-function orprogrammable), and in some examples, one or more of the units may beintegrated circuits.

The angular coding mode (i.e., the angular mode) was adopted at the129th MPEG meeting in Brussels, Belgium. The following descriptions arebased on the original MPEG contributions documents: Sébastien Lasserre,Jonathan Taquet, “[GPCC][CE 13.22 related] An improvement of the planarcoding mode,” ISO/IEC JTC1/SC29/WG11 MPEG/m50642, Geneva, CH, October2019; and w19088. The angular coding mode is optionally used togetherwith planar mode (e.g., as described in Sebastien Lasserre, David Flynn,“[GPCC] Planar mode in octree-based geometry coding,” ISO/IECJTC1/SC29/WG11 MPEG/m48906, Gothenburg, Sweden, July 2019) and improvesthe coding of the vertical (z) plane position syntax element byemploying knowledge of positions and angles of sensing laser beams in atypical LIDAR sensor (see e.g., Sebastien Lasserre, Jonathan Taquet,“[GPCC] CE 13.22 report on angular mode,” ISO/IEC JTC1/SC29/WG11MPEG/m51594, Brussels, Belgium, January 2020).

FIG. 4 is a conceptual diagram illustrating example planar occupancy ina vertical direction. In the example of FIG. 4 , a node 400 ispartitioned into eight child nodes. Child nodes 402 may be occupied orunoccupied. In the example of FIG. 4 , occupied child nodes are shaded.When one or more child nodes 402A-402D are occupied and none of childnodes 402E-402H are occupied, G-PCC encoder 200 may signal a planeposition (planePosition) syntax element with a value of 0 to indicatethat all occupied child nodes are adjacent on a positive side (i.e., aside of increasing z-coordinates) of a plane of the minimum z coordinateof node 400. When one or more child nodes 402E-402H are occupied andnone of child nodes 402A-402D are occupied, G-PCC encoder 200 may signala plane position (planePosition) syntax element with a value of 1 toindicate that all occupied child nodes are adjacent on a positive sideof a plane of a midpoint z coordinate of node 400. In this way, theplane position syntax element may indicate a vertical plane position ofa planar mode in node 400.

Furthermore, the angular coding mode can optionally be used to improvethe coding of vertical z-position bits in Inferred Direct Coding Mode(IDCM) (Sebastien Lasserre, Jonathan Taquet, “[GPCC] CE 13.22 report onangular mode,” ISO/IEC JTC1/SC29/WG11 MPEG/m51594, Brussels, Belgium,January 2020). IDCM is a mode in which the positions of points within anode are explicitly (directly) signaled relative to a point within anode. In the angular coding mode, the positions of points may besignaled relative to an origin point of the node.

The angular coding mode may be used when the point cloud is generatedbased on data generated by a range-finding system, such as a LIDARsystem. The LIDAR system may include a set of lasers arrayed in avertical plane at different angles relative to an origin point. TheLIDAR system may rotate around a vertical axis. The LIDAR system may usereturned laser light to determine the distances and positions of pointsin the point cloud. The laser beams emitted by the lasers of a LIDARsystem may be characterized by a set of parameters.

The following describes signaling of sensor laser beam parameters inw19088 for angular mode. The syntax elements that carry the LIDAR lasersensor information that may be required for the angular coding mode tohave any coding efficiency benefits are indicated using <!> . . . </!>tags in Table 1, below. In Table 1, angular mode syntax elements areindicated with <!> . . . </!> tags in a geometry parameter set.

TABLE 1 Descriptor geometry_parameter_set( ) { ... . . .geometry_planar_mode_flag u(1) if( geometry_planar_mode_flag ){geom_planar_mode_th_idcm ue(v) geom_planar_mode_th[ 1 ] ue(v)geom_planar_mode_th[ 2 ] ue(v) } <!> geometry_angular_mode_flag </!>u(1) <!> if( geometry_angular_mode_flag ){</!> <!>lidar_head_position[0]</!> se(v) <!> lidar_head_position[1]</!> se(v)<!> lidar_head_position[2]</!> se(v) <!> number_lasers</!> ue(v) <!>for( i = 0; i < number_lasers; i++ ) {</!> <!> laser_angle[ i ]</!>se(v) <!> laser_correction[ i ]</!> se(v) <!> }</!> <!>planar_buffer_disabled</!> u(1) <!> se(v)implicit_qtbt_angular_max_node_min_dim_log2_to_split_z< /!> <!>implicit_qtbt_angular_max_diff_to_split_z</!> se(v) <!> }</!>neighbour_context_restriction_flag u(1)inferred_direct_coding_mode_enabled_flag u(1) ...

The semantics of these syntax elements are specified as follows inw19088:

geometry_planar_mode_flag equal to 1 indicates that the planar codingmode is activated. geometry_planar_mode_flag equal to 0 indicates thatthe planar coding mode is not activated.

geom_planar_mode_th_idcm specifies the value of the threshold ofactivation for the direct coding mode. geom_planar_mode_th_idcm is aninteger in the range 0 to 127 inclusive. When not present,geom_planar_mode_th_idcm is inferred to be 127.

geom_planar_mode_th[i], for i in the range 0 . . . 2, specifies thevalue of the threshold of activation for planar coding mode along thei-th most probable direction for the planar coding mode to be efficient.geom_planar_mode_th[i] is an integer in the range 0 . . . 127.geometry_angular_mode_flag equal to 1 indicates that the angular codingmode is activated. geometry_angular_mode_flag equal to 0 indicates thatthe angular coding mode is not activated.lidar_head_position[ia], for is in the range 0 . . . 2, specifies theia-th coordinate of the lidar head in the coordinate system associatedwith the internal axes. When not present, lidar_head_position[ia] isinferred to 0.number_lasers specifies the number of lasers used for the angular codingmode. When not present, number_lasers is inferred to 0.laser_angle[i], for i in the range 1 . . . number_lasers, specifies thetangent of the elevation angle of the i-th laser relative to thehorizontal plane defined by the 0-th and the 1-th internal axes.laser_correction [i], for i in the range 1 . . . number_lasers,specifies the correction, along the 2-th internal axis, of the i-thlaser position relative to the lidar head positionlidar_head_position[2]. When not present, laser_correction [i] isinferred to 0.planar_buffer_disabled equal to 1 indicates that tracking the closestnodes using a buffer is not used in a process of coding the planar modeflag and the plane position in the planar mode. planar_buffer_disabledequal to 0 indicates that tracking the closest nodes using a buffer isused. When not present, planar_buffer_disabled is inferred to 0.implicit_qtbt_angular_max_node_min_dim_log 2_to_split_z specifies thelog 2 value of a node size below which horizontal split of nodes ispreferred over vertical split. When not present,implicit_qtbt_angular_max_diff_to_split_z is inferred to 0.implicit_qtbt_angular_max_diff_to_split_z specifies the log 2 value ofthe maximum vertical over horizontal node size ratio allowed to a node.When not present, implicit_qtbt_angular_max_node_min_dim_log2_to_split_z is inferred to 0.

Only some nodes in an octree may be eligible to be coded using theangular mode. The following describes a process for determining nodeeligibility for angular mode in w19088. The process applies to a childnode Child to determine the angular eligibility angular_eligible[Child]of the child node. In w19088, a syntax elementgeometry_angular_mode_flag indicates whether the angular mode is active.If geometry_angular_mode_flag is equal to 0, angular_eligible[Child] isset to equal to 0. Otherwise, the following applies:

midNodeX = 1 << (ChildNodeSizeXLog2 − 1) midNodeY = 1 <<(ChildNodeSizeXLog2 − 1) xLidar = abs( ((xNchild −lidar_head_position[0] + midNodeX) << 8) − 128 ) yLidar = abs( ((yNchild− lidar_head_position[1] + midNodeY) << 8) − 128 ) rL1 = (xLidar +yLidar) >> 1 deltaAngleR = deltaAngle*rL1 midNodeZ = 1 <<(ChildNodeSizeZLog2 − 1) if (deltaAngleR <= (midNodeZ << 26))angular_eligible[Child] = 0 else angular_eligible[Child] = 1

-   -   where deltaAngle is the minimum angular distance between the        lasers determined by:        deltaAngle=min{|laser_angle[i]−laser_angle[j]|;0≤i<j<number_lasers},    -   and where (xNchild, yNchild, zNchild) specifies the position of        the geometry octree child node Child in the current slice.

The following process described in w19088 applies to a child node Childto determine the IDCM angular eligibility idcm4angular[Child] and thelaser index laserIndex [Child] associated with the child node. If theangular eligibility angular_eligible[Child] is equal to 0, thenidcm4angular[Child] is set to 0 and laserIndex [Child] index is set to apre-set value UNKNOWN_LASER. Otherwise, if the angular eligibilityangular_eligible[Child] is equal to 1, the following applies as acontinuation of the process described in section 8.2.5.1 of w19088.Firstly, the inverse rInv of the radial distance of the child node fromthe Lidar is determined:

r2 = xLidar*xLidar + yLidar*yLidar rInv = invSqrt (r2)

-   -   Then, an angle theta32 is determined for the child node:

zLidar = ((zNchild − lidar_head_position [2] + midNodeY) << 1) − 1 theta= zLidar*rInv theta32 = theta >= 0 ? theta >> 15 : −((−theta) >> 15)rInv may correspond to the inverse of the radial distance of the childnode. The angle theta32 may correspond to a tangent of the elevationangle of a midpoint of the child node. Finally, the angular eligibilityand the laser associated with the child node are determined as shown inTable 2, below, based on the parent node Parent of the child node:

TABLE 2 laserIndex [Child] = UNKNOWN_LASER idcm4angular[Child] = 0 if(laserIndex [Parent] == UNKNOWN_LASER ∥ deltaAngleR <= (midNodeZ<< (26 +2))) { minDelta = 1 << (18 + 7) for (j = 0; j < number_lasers; j++) {delta = abs(laser_angle [j] − theta32) if (delta < minDelta) { minDelta= delta laserIndex [Child] =j } } } else idcm4angular[Child] = 1

One type of angular mode enhancement for planar mode involvesdetermination of a context contextAngular for planar coding mode.Specifically, the following process applies to a child node Child todetermine the angular context contextAngular[Child] associated with thechild node. If the laser index laserIndex[Child] is equal toUNKNOWN_LASER, then contextAngular[Child] is set to a pre-set valueUNKNOWN_CONTEXT. Otherwise, if the laser index laserIndex [Child] is notequal to UNKNOWN_LASER, the following applies as a continuation of theprocess described in section 8.2.5.2 of w19088. Firstly, two angulardifferences m and M relative to a lower plane and an upper plane aredetermined.

thetaLaserDelta = laser_angle [laserIndex [Child]] − theta32 Hr =laser_correction [laserIndex [Child]] * rInv; thetaLaserDelta += Hr >= 0? −(Hr >> 17) : ((−Hr) >> 17) zShift = (rInv << (ChildNodeSizeZLog2 +1)) >> 17 m = abs(thetaLaserDelta − zShift) M = abs(thetaLaserDelta +zShift)Then, the angular context is deduced from the two angular differences:

contextAngular[Child] = m > M ? 1 : 0 diff = abs(m − M) if (diff >=rInv >> 15) contextAngular[Child] += 2; if (diff >= rInv >> 14)contextAngular[Child] += 2; if (diff >= rInv >> 13)contextAngular[Child] += 2; if (diff >= rInv >> 12)contextAngular[Child] += 2;

The term thetaLaserDelta may be a laser difference angle determined bysubtracting a tangent of the angle of the line passing through thecenter of the node from a tangent of an angle of the laser beam.

Another type of angular mode enhancement for IDCM described in w19088involves determination of the angular context idcmIdxAngular.Specifically, a process to determine the context idcmIdxAngular[i][j]for coding the bin point_offset_z[i][j] associated with the j-th bit ofthe i-th point belonging to a child node that undergoes Inferred DirectCoding Mode is described as follows.

This process is performed after point_offset_x[i][ ] andpoint_offset_y[i][ ] are decoded such that PointOffsetX[i] andPointOffsetY[i] are known. The x and y position relative to the Lidar,of the point i is derived by:

posXlidar[i] = xNchild − lidar_head_position[0] + PointOffsetX[ i ]posYlidar[i] = yNchild − lidar_head_position[1] + PointOffsetY[ i ]where (xNchild, yNchild, zNchild) specify the position of the geometryoctree child nodeChild in the current slice.The inverse rInv of the radial distance of the point from the LIDAR isdetermined by:

xLidar = (posXlidar[i] << 8) − 128 yLidar = (posYlidar[i] << 8) − 128 r2= xLidar*xLidar + yLidar*yLidar rInv = invSqrt (r2)The corrected laser angle ThetaLaser of the laser associated with thechild nodeChild is deduced as follows:

Hr = laser_correction [laserIndex [Child]] * rInv ThetaLaser =laser_angle [laserIndex [Child]] + (Hr >= 0 ? −(Hr >> 17) : ((−Hr) >>17))Assuming that the bits point_offset_z[i][j2] for j2 in the range 0 . . .j−1 are known, the point is known to belong to a virtual verticalinterval whose half size is given by:halfIntervalSize[j]=(1<<(EffectiveChildNodeSizeZ Log 2−1))>>jand a partial z point position posZlidarPartial[i][j], that provides thelower end of the interval, is deduced by:

PointOffsetZpartial = 0 for( j2 = 0; j2 < j; j2++ ) PointOffsetZpartial[ i ] += point_offset_z[ i ][ j2 ] << j2 PointOffsetZpartial[ i ] <<=(EffectiveChildNodeSizeZLog2−j) posZlidarPartial[i][j] = zNchild −lidar_head_position[2] + PointOffsetZpartial[ i ]A relative laser position thetaLaserDeltaVirtualInterval relative to themiddle of the virtual interval is computed by:

zLidar = ((posZlidarPartial[i][j] + halfIntervalSize [j]) << 1) − 1theta = zLidar*rInv theta32 = theta >= 0 ? theta >> 15 : −((−theta) >>15) thetaLaserDeltaVirtualInterval = ThetaLaser − theta32Two absolute angular differences m and M of the laser relative to alower and an upper z position in the virtual interval are determined:

zShift = ((rInv << EffectiveChildNodeSizeZLog2) >> 17) >> j m =abs(thetaLaserDeltaVirtualInterval − zShift); M =abs(thetaLaserDeltaVirtualInterval + zShift);Then, the angular context is deduced from the two absolute angulardifferences:

idcmIdxAngular[i][j] = m > M ? 1: 0 diff = abs(m − M) if (diff >=rInv >> 15) idcmIdxAngular[i][j] += 2 if (diff >= rInv >> 14)idcmIdxAngular[i][j] += 2 if (diff >= rInv >> 13) idcmIdxAngular[i][j]+= 2 if (diff >= rInv >> 12) idcmIdxAngular[i][j] += 2

When IDCM is applied to a child node Child, the bitspoint_offset_z[i][j] of the i-th point in the child node, for j in therange 0 . . . EffectiveChildNodeSizeZ Log 2 or in the range 1 . . .EffectiveChildNodeSizeZ Log 2 in case the first bit is inferred by theplane position plane_position[Child] [2], are decoded applying thefollowing process. If geometry_angular_mode_flag is equal to 0, then thebit point_offset_z[i][j] is decoded using the bypass decoding process.Otherwise, if geometry_angular_mode_flag is equal to 1, the bitpoint_offset_z[i][0] is bypass decoded when not inferred by the planeposition, and the bits point_offset_z[i][j] are decoded using thecontext idcmIdxAngular[i][j] for j>0.

As specified above, the determination of the angular context indices forcoding the planar mode's vertical plane position and for coding the IDCMvertical point position offsets involves significant complexity. Suchcomplexity may present technical problems because the complexity mayincrease hardware costs, slow the coding process, and/or have othernegative consequences. For example, 10 contexts are used for coding theplanar mode's vertical plane position. In another example, 10 contextsare used for coding IDCM's vertical point position offsets per bin. Inanother example, 5 conditions based on comparisons of two large integervalues are used to determine each context index. In another example,inverse square root distance computation is required (rInv in spec textabove). In another example, the signaling of the number_lasers syntaxelement starts from the zero value, while there is at least one laserrequired for the angular mode. Furthermore, there are variousinefficiencies in signaling angular mode syntax elements.

This disclosure describes techniques that may address one or more ofthese technical problems. The techniques and examples disclosed in thisdocument may be applied independently or in combination.

In accordance with techniques of this disclosure, the number of contextsfor coding the planar mode's vertical plane position is reduced. In thefollowing descriptions, a laser, laser beam, laser sensor or sensor, orother similar terms may represent any sensor that can return a distancemeasure and a spatial orientation, including potentially an indicationof time, for example, a typical LIDAR sensor.

A G-PCC coder (e.g., G-PCC encoder 200 or G-PCC decoder 300) may codethe planar mode's vertical plane position in a node by selecting a laserindex out of a set of laser candidates that are signaled in a parameterset, such as the geometry parameter set, with the selected laser indexindicating the laser beam that intersects the node. The intersection ofthe laser beam with the node determines the context index toarithmetically code the planar mode's vertical plane position. In thefollowing descriptions, this principle is referred to as angular modecoding.

Thus, in some examples, a G-PCC coder (e.g., G-PCC encoder 200 or G-PCCdecoder 300) may code a vertical plane position of a planar mode in anode of an octree that represents 3-dimensional positions of points inthe point cloud. For ease of explanation, this disclosure may refer to anode that the G-PCC coder is coding as a current node. As part of codingthe vertical plane position of the planar mode, the G-PCC coder maydetermine a laser index of a laser candidate in a set of lasercandidates. The determined laser index indicates a laser beam thatintersects the current node. The set of laser candidates may includeeach of the lasers in a LIDAR array. In some examples, the set of lasercandidates may be indicated in a parameter set, such as a geometryparameter set. Additionally, as part of coding the vertical planeposition, the G-PCC coder determines a context index based on anintersection of the laser beam and the current node. For instance, theG-PCC coder may determine a context index based on whether the laserbeam is above a first distance threshold, between the first distancethreshold and a second distance threshold, between the second distancethreshold and a third distance threshold, or below the third distancethreshold. Furthermore, as part of coding the vertical plane position,the G-PCC coder arithmetically codes the vertical plane position of theplanar mode using a context indicated by the determined context index.

There may be an eligibility condition to determine whether the planarmode's vertical plane position in the current node is eligible to becoded using the angular mode. If the vertical plane position is noteligible to be coded using the angular mode, the planar mode's verticalplane position may be coded without employing sensor information. Insome examples, the eligibility condition may determine whether only onelaser beam intersects the current node. In other words, the verticalplane position of the current node may be eligible to be coded using theangular mode if only one laser beam intersects the current node. In someexamples, the eligibility condition may determine the minimum angledifference between the lasers out of the set of laser candidates. Inother words, the current node may be eligible to be coded using theangular mode if the angle enveloping the current node is less than theminimum angle between laser beams. The angle enveloping the current nodeis an angle measured from the laser origin between a line passingthrough a far, bottom corner of the node and a line passing through anear, top corner of the node. When the angle enveloping the current nodeis less than the minimum angle difference between laser beams, only onelaser beam intersects the node. In some examples, the eligibilitycondition is such that the vertical node dimension is smaller than (orequal to) the minimum angle difference. In other words, the current nodemay be eligible to be coded using the angular mode if the verticaldimension of the node is less than or equal to a vertical distancebetween laser beams separated by the minimum angle difference at theclosest vertical edge of the current node to the laser origin.

As noted above, the G-PCC coder may select a laser index of a laser beamthat intersects the current node. In some examples, the G-PCC coder maydetermine the index of the laser that intersects the current node byselecting a laser beam that is nearest to a marker point in the currentnode. In some examples, the marker point in the current node may be thecenter point of the current node with coordinates at half of all threedimensions of the current node (for example, cube or cuboid dimensions).In other examples, the marker point in the current node may be any otherpoint that is part of the current node, such as any point within thenode or on the node sides, or on the node edges, or node corners.

The G-PCC coder may determine whether a laser is near the marker pointbased on comparing angular differences of the candidate lasers. Forexample, the G-PCC coder may compare differences between angles of thelaser beams and an angle of the marker point. The angles of the laserbeams may be defined as being between the horizontal plane (z=0) and thedirection of the laser beam. The angle of the marker point may bedefined as being between the horizontal plane and the direction of avirtual beam to the marker point. The origin in this case may becollocated with the center of the sensor or laser. Alternatively, insome examples, mathematical functions or trigonometric functions such asthe tangent may be applied to the angles before the comparison.

In some examples, the G-PCC coder may determine whether a laser is nearthe marker point based on a comparison of vertical coordinatedifferences. For example, the G-PCC coder may compare the marker point'svertical coordinate with respect to the sensor origin (e.g., thez-coordinate of the marker point) and the vertical coordinate of thelaser intersection with the node. The G-PCC coder may obtain thevertical coordinate of the laser intersection with the node bymultiplying the tangent of the angle between the horizontal plane andthe laser direction with a distance computed by taking a Euclideandistance based on the (x,y) coordinates of the marker point(trigonometry).

In some examples, the G-PCC coder may determine a context index to useto code a vertical plane position syntax element based on a relativeposition of the laser beam and the marker point. For example, the G-PCCcoder may determine that a context index is a first context index if thelaser beam is above the marker point and may determine that the contextindex is a second context index if the laser beam is below the markerpoint. The G-PCC coder may determine whether the laser beam is above orbelow the marker point in a similar fashion as determining a laser beamindex that intersects with the node, e.g., by comparing angulardifferences, comparing differences of tangent of angle values, orcomparing vertical coordinate differences.

In some examples, as part of determining the context index, the G-PCCcoder may determine distance threshold values. The G-PCC coder may usethe distance threshold values to compare the distance between the laserbeam and the marker point. The distance threshold values may divide adistance range within the current node into intervals. The intervals maybe of equal or unequal lengths. Each interval of the intervals maycorrespond to a context index if the laser beam is within the distancerange determined by the distance intervals. In some examples, there aretwo distance thresholds determined by equal distance offsets above andbelow the marker point, which define three distance intervals thatcorrespond with three context indexes. The G-PCC coder may determinewhether the laser beam belongs to an interval in a similar fashion asdetermining the laser beam index of a laser that intersects with thenode (e.g., by comparing angular differences, comparing differences oftangent of angle values, or comparing vertical coordinate differences).

The above principles, which employ sensor information, are not limitedto coding the planar mode's vertical (Z) plane position syntax elementwithin a node, but similar principles may also be applied to coding theplanar mode's X or Y plane position syntax elements within a node. Theplanar mode's X or Y plane position modes may be chosen by an encoder ifthey are more appropriate to code the point distribution within thenode. For instance, if occupied child nodes are all on one side of aplane oriented in the X direction, an X plane position syntax elementmay be used to code the point distribution within the node. If occupiedchild nodes are all on one side of a plane oriented in the Y direction,a Y plane position syntax element may be used to code the pointdistribution within the node. Additionally, combinations of two or moreplanes oriented in the X, Y, or Z directions may be used to indicateoccupancy of child nodes.

The G-PCC coder may determine a context from among two contexts based onwhether the laser beam is above or below the marker point (i.e., ispositioned above or below the marker point). In this example, the markerpoint is the center of the node. This is illustrated in FIG. 5 . Morespecifically, FIG. 5 is a conceptual diagram of an example in which acontext index is determined based on laser beam positions 500A, 500Babove or below a marker point 502 of a node 504, in accordance with oneor more techniques of this disclosure. Thus, in the example of FIG. 5 ,if the laser that intersects node 504 is above marker point 502, asshown with respect to laser beam position 500A, the G-PCC coder selectsa first context index (e.g., Ctx=0). In the example of FIG. 5 , markerpoint 502 is located at a center of node 504. If the laser thatintersects node 504 is below marker point 502, as shown with respect tolaser beam position 500B, the G-PCC coder selects a second context index(e.g., Ctx=1).

As discussed in above, w19088 describes a context determination methodthat involves determination of a context contextAngular for planarcoding mode. The text of w19088 associated with this contextdetermination method is reproduced in Table 3, below. More specifically,Table 3 shows the specification text of w19088 for determining a contextindex out of a set of 10 possible context indexes. The contextdetermination method provided in w19088 evaluates five conditions basedon comparisons of large integer values to determine the context index inthe range 0 to 9. These comparisons are indicated with <!> . . . </!>tags in Table 3.

TABLE 3 Firstly, two angular differences m and M relative to a lowerplane and an upper plane are determined. thetaLaserDelta = laser_angle[laserIndex [Child]] − theta32 Hr = laser_correction [laserIndex[Child]] * rInv thetaLaserDelta += Hr >= 0 ? −(Hr >> 17) : ((−Hr) >> 17)zShift = (rInv << (ChildNodeSizeZLog2 + 1)) >> 17 m =abs(thetaLaserDelta − zShift) M = abs(thetaLaserDelta + zShift) Then,the angular context is deduced from the two angular differences.contextAngular[Child] = <!>m > M ?</!> 1 : 0 diff = abs(m − M) <!>if(diff >= rInv >> 15)</!> contextAngular[Child] += 2 <!>if (diff >=rInv >> 14)</!> contextAngular[Child] += 2 <!>if (diff >= rInv >>13)</!> contextAngular[Child] += 2 <!>if (diff >= rInv >> 12)</!>contextAngular[Child] += 2

The simplified two-context derivation proposed in this disclosure isspecified in Table 4, below. More specifically, Table 4 shows an exampleproposed simplified two-context derivation. The number of conditions isreduced to one, which is indicated with <!> . . . </!> tags. This singlecondition may be easy to implement because the single condition may beto only check the sign of an integer value.

TABLE 4 Firstly, the corrected angular difference relative to the centerof the node is determined. thetaLaserDelta = laser_angle [laserIndex[Child]] − theta32 Hr = laser_correction [laserIndex [Child]] * rInvthetaLaserDelta += Hr >= 0 ? −(Hr >> 17) : ((−Hr) >> 17) Then, theangular context is deduced from the sign of thetaLaserDelta.contextAngular[Child] = 0 <!>if (thetaLaserDelta < 0)</!>contextAngular[Child] = 1

In Table 4, and elsewhere in this disclosure, laserIndex[Child] is anindex of a laser beam that is determined to be closest to the centerpoint of a node “Child”; laser_angle indicates a tangent of an angle ofthe laser beam determined to be closest to the center point of the node“Child”; thetaLaserDelta indicates the difference between a tangent ofthe angle of the laser beam determined to be closest to the center pointof the node “Child” (theta32) and an offset correction; laser_correctionindicates a laser correction factor; Hr indicates a tangent of an anglecorresponding to vertical offset correction, contextAngular indicates acontext; and theta32 may be determined as described elsewhere in thisdisclosure. The operation thetaLaserDelta+=Hr>=0 ?−(Hr>>17): ((−Hr)>>17)provides a precision adjustment.

In some examples, the G-PCC coder determines three contexts based on thelaser beam being positioned above or below two distance thresholds, orin between the distance thresholds. In this example, the marker point isthe center of the node. This is illustrated in FIG. 6 . Morespecifically, FIG. 6 is a conceptual diagram illustrating an examplethree-context index determination for a node 600, in accordance with oneor more techniques of this disclosure. In FIG. 6 , the distance intervalthresholds are indicated with fine-dotted lines 602A, 602B. Laser beamsare indicated with solid lines 604A, 604B. Each of these laser beams maybe a laser candidate. A center point 606 (marker point) is indicatedwith a white circle. Thus, in the example of FIG. 6 , if a laser (suchas the laser corresponding to line 604A) is above line 602A, the G-PCCcoder selects the context index ctx1. If a laser is between lines 602A,602B, the G-PCC coder may select the context index ctx0. If a laser(such as the laser corresponding to line 604B) is below line 602B, theG-PCC coder may select the context index ctx2.

The simplified specification text is provided in Table 5, below. Morespecifically, Table 5 shows a proposed simplified three-contextderivation. In this example, the G-PCC coder uses two conditions, whichmay be easy to implement because the conditions only check the signs ofinteger values.

TABLE 5 Firstly, two angular differences m and M relative to a lowerplane and an upper plane are determined. thetaLaserDelta = laser_angle[laserIndex [Child]] − theta32 Hr = laser_correction [laserIndex[Child]] * rInv thetaLaserDelta += Hr >= 0 ? −(Hr >> 17) : ((−Hr) >> 17)zShift = (rInv << ChildNodeSizeZLog2) >> 20 DeltaBottom =thetaLaserDelta + zShift DeltaTop = thetaLaserDelta − zShift Then, theangular context is deduced. contextAngular[Child] = 0 <!>if (DeltaTop >=0)</!> contextAngular[Child] = 1 <!>else if (DeltaBottom < 0)</!>contextAngular[Child] = 2

In Table 5, and elsewhere in this disclosure, ChildNodeSizeZ Log 2 isthe log base 2 of a height (z-distance) of the node Child. DeltaTop maybe a top angle difference determined by subtracting a shift value fromthe laser difference angle. DeltaBottom may be a bottom angle differenceby adding the shift value to the laser difference angle. The term zShiftmay be based on an angle between top and bottom corners of the nodeChild and scaled to a smaller interval.

In some examples, the G-PCC coder uses four contexts for coding theplanar mode's vertical plane position in the case that the angular modeis used. In such examples, the G-PCC coder may determine the contextindex based on a laser beam's position within four intervals. Thisexample is illustrated in FIG. 7 . FIG. 7 is a conceptual diagramillustrating an example context index determination for coding a planarmode's vertical plane position (angular mode) based on a laser beamposition (solid arrow) with intervals separated by finely dotted lines.In the example of FIG. 7 , lines 700A, 700B, and 700C correspond todistance interval thresholds. Furthermore, in the example of FIG. 7 , amarker point 702 is located at a center of node 704. Line 706corresponds to a laser beam that intersects node 704. Because line 706is above line 700A, the G-PCC coder may select a context index, ctx2,for use in coding the vertical plane position.

The proposed simplified four-context derivation is specified in Table 6,below. The number of conditions is reduced to three, which are indicatedwith <!> . . . </!> tags. These conditions may be simple to implementbecause they only check the sign of an integer value. Table 6 showsexample specification text of a proposed simplified four-contextderivation for coding planar mode's vertical plane position (angularmode case).

TABLE 6 ... Firstly, the corrected angular difference relative to thecenter of the node is determined. thetaLaserDelta = laser_angle[laserIndex [Child]] − theta32 Hr = laser_correction [laserIndex[Child]] * rInv thetaLaserDelta += Hr >= 0 ? −(Hr >> 17) : ((−Hr) >> 17)Then, the angular context is deduced. zShift = (rInv <<ChildNodeSizeZLog2) >> 20 DeltaBottom = thetaLaserDelta + zShiftDeltaTop = thetaLaserDelta − zShift contextAngular[Child] =<!>thetaLaserDelta >= 0</!> ? 0 : 1 if (<!>DeltaTop >= 0</!>)contextAngular[Child] += 2 else if (<!>DeltaBottom < 0</!>)contextAngular[Child] += 2 ...

FIG. 8A is a flowchart illustrating an example operation for encoding avertical plane position in accordance with one or more techniques ofthis disclosure. G-PCC encoder 200 may perform the operation of FIG. 8Aas part of encoding a point cloud.

In the example of FIG. 8A, G-PCC encoder 200 (e.g., arithmetic encodingunit 214 of G-PCC encoder 200 (FIG. 2 ) may encode a vertical planeposition of a planar mode in a node of a tree (e.g., an octree) thatrepresents 3-dimensional positions of points in a point cloudrepresented by point cloud data (800). In other words, G-PCC encoder 200may encode the vertical plane position.

As part of encoding the vertical plane position of the planar mode,G-PCC encoder 200 (e.g., arithmetic encoding unit 214) may determine alaser index of a laser candidate in a set of laser candidates, whereinthe determined laser index indicates a laser beam that intersects thenode (802). G-PCC encoder 200 may determine the laser index inaccordance with any of the examples provided elsewhere in thisdisclosure. For example, G-PCC encoder 200 may determine the laser indexas shown in Table 2, above.

Additionally, G-PCC encoder 200 (e.g., arithmetic encoding unit 214 maydetermine a context index (contextAngular) based on whether the laserbeam is above a first distance threshold, between the first distancethreshold and a second distance threshold, between the second distancethreshold and a third distance threshold, or below the third distancethreshold (804). For instance, in the example of FIG. 7 , G-PCC encoder200 may determine the context index based on whether the laser beam isabove a first distance threshold (corresponding to line 700A), betweenthe first distance threshold and a second distance threshold(corresponding to line 700B), between the second distance threshold anda third distance threshold (corresponding to line 700C), or below thethird distance threshold. In some examples, to determine the position ofthe laser beam relative to the first, second, and third distancethresholds, G-PCC encoder 200 may determine a laser difference angle(e.g., thetaLaserDelta) by subtracting a tangent of the angle of theline passing through the center of the node from a tangent of an angleof the laser beam, determine a top angle difference (e.g., DeltaTop) bysubtracting a shift value from the laser difference angle; and determinea bottom angle difference (e.g., DeltaBottom) by adding the shift valueto the laser difference angle.

G-PCC encoder 200 may perform a first comparison that determines whetherthe laser difference angle is greater than or equal to 0 (e.g.,thetaLaserDelta>=0). G-PCC encoder 200 may set the context index to 0 or1 based on whether the laser difference angle is greater than or equalto 0 (e.g., contextAngular[Child]=thetaLaserDelta>=0? 0:1. Additionally,G-PCC encoder 200 may perform a second comparison (e.g., DeltaTop>=0)that determines whether the top angle difference is greater than orequal to 0. The laser beam is above the first distance threshold whenthe top angle difference is greater than or equal to 0. G-PCC encoder200 may also perform a third comparison (e.g., DeltaBottom<0) thatdetermines whether the bottom angle difference is less than 0. The laserbeam is below the third distance threshold when the bottom angledifference is less than 0. G-PCC encoder 200 may increment the contextindex by 2 based on the top angle difference being greater than or equalto 0 (e.g., if (DeltaTop>=0) contextAngular[Child]+=2) or based on thebottom angle difference being less than 0 (e.g., else if (DeltaBottom<0)contextAngular[Child]+=2).

G-PCC encoder 200 (e.g., arithmetic encoding unit 214 of G-PCC encoder200) may arithmetically encode the vertical plane position of the planarmode using a context indicated by the determined context index (806).For example, G-PCC encoder 200 may perform CABAC encoding on a syntaxelement indicating the vertical plane position.

FIG. 8B is a flowchart illustrating an example operation for decoding avertical plane position in accordance with one or more techniques ofthis disclosure. G-PCC decoder 300 may perform the operation of FIG. 8Bas part of reconstructing a point cloud represented by point cloud data.In the example of FIG. 8B, G-PCC decoder 300 (e.g., geometry arithmeticdecoding unit 302 of FIG. 3 ) may decode a vertical plane position of aplanar mode in a node of a tree (e.g., an octree) that represents3-dimensional positions of points in the point cloud (850). In otherwords, G-PCC decoder 300 may decode the vertical plane position.

As part of decoding the vertical plane position of the planar mode,G-PCC decoder 300 (e.g., geometry arithmetic decoding unit 302) maydetermine a laser index of a laser candidate in a set of lasercandidates, wherein the determined laser index indicates a laser beamthat intersects the node (852). G-PCC decoder 300 may determine thelaser index in accordance with any of the examples provided elsewhere inthis disclosure. For example, G-PCC decoder 300 may determine the laserindex as shown in Table 2, above.

Additionally, G-PCC decoder 300 may determine a context index(contextAngular) based on whether the laser beam is above a firstdistance threshold, between the first distance threshold and a seconddistance threshold, between the second distance threshold and a thirddistance threshold, or below the third distance threshold (804). G-PCCdecoder 300 may determine the context index in the same manner as G-PCCencoder 200, as described above.

G-PCC decoder 300 (e.g., geometry arithmetic decoding unit 302 of G-PCCdecoder 300) may arithmetically decode the vertical plane position ofthe planar mode using a context indicated by the determined contextindex (806). For example, G-PCC decoder 300 may perform CABAC decodingon a syntax element indicating the vertical plane position. In someexamples, G-PCC decoder 300 may determine positions of one or morepoints in the point cloud based on the vertical plane position. Forinstance, G-PCC decoder 300 may determine, based on the vertical planeposition, locations of occupied child nodes of a node. G-PCC decoder 300may then process the occupied child nodes to determine positions ofpoints within the occupied child nodes and may not need to performfurther processing on the unoccupied child nodes.

As mentioned above, the one or more techniques of this disclosure mayreduce the number of contexts for coding IDCM vertical point positionoffsets. A G-PCC coder (e.g., G-PCC encoder 200 or G-PCC decoder 300)may code (i.e., encode or decode) a IDCM's vertical point positionoffset within a node in part by selecting a laser index out of a set oflaser candidates. The set of laser candidates may be signaled in aparameter set, such as the geometry parameter set, with the selectedlaser index indicating the laser beam that intersects the node. The setof laser candidates may correspond to lasers in a LIDAR array. The G-PCCcoder may determine the context indexes to arithmetically code the bins(bits) from the IDCM's vertical point position offset based on theintersection of a laser beam with a node.

Thus, in some examples, a G-PCC coder (e.g., G-PCC encoder 200 or G-PCCdecoder 300) may code a vertical point position offset within a node ofa tree (e.g., an octree) that represents 3-dimensional positions ofpoints in the point cloud. As part of coding the vertical point positionoffset, the G-PCC coder may determine a laser index of a laser candidatein a set of laser candidates. The determined laser index indicates alaser beam that intersects the node. Additionally, as part of coding thevertical position offset, the G-PCC coder may determine a context indexbased on an intersection of the laser beam and the node. The G-PCC codermay arithmetically code bins of the vertical point position offset usinga context indicated by the determined context index.

In some examples, there may be an eligibility condition that the G-PCCcoder may use to determine whether a node is eligible for using theangular mode to code the IDCM's vertical point position offset. If thevertical point position offset is not eligible to be coded in the nodeusing the angular mode, the G-PCC coder may code the IDCM's verticalpoint position offset without employing sensor information. In someexamples, the eligibility condition may determine whether only one laserintersects the node. In other words, the G-PCC coder may determine thatthe vertical point offset is eligible to be coded in the node using theangular mode if only one laser intersects the node.

In some examples, the eligibility condition may determine the minimumangle difference between the lasers out of the set of laser candidates.In other words, the G-PCC coder may determine that the vertical pointoffset is eligible to be coded in the node using the angular mode ifthere is at least a minimum angle between lasers in the set of lasercandidates. In some examples, the eligibility condition is such that thevertical node dimension is smaller than (or equal to) the minimum angledifference. In other words, the G-PCC coder may determine that thevertical point offset is eligible to be coded in the node using theangular mode if the vertical node dimension is less than or equal to theminimum angle difference. In other words, the current node may beeligible to be coded using the angular mode if the vertical dimension ofthe node is less than or equal to a vertical distance between laserbeams separated by the minimum angle difference at the closest verticaledge of the current node to the laser origin.

In some examples, the G-PCC coder determines the index of the laser thatintersects the node by selecting a laser beam that is nearest to amarker point in the node. The marker point in the node may be the centerpoint of the node, for example, with coordinates at half of all threedimensions of the node (for example cube or cuboid dimensions), or anyother point that is part of the node, such as any point within the nodeor on the node sides, or on the node edges, or node corners.

The G-PCC coder may determine whether a laser is near the marker pointbased on a comparison of angular differences. For example, the G-PCCcoder may compare the difference between the angles of the laser beamsand an angle of the marker point. The angles of the laser beams may bedefined as being between the horizontal plane (z=0) and the direction ofthe laser beam. The angle of the marker point may be defined as beingthe angle between the horizontal plane and the direction of a virtualbeam to the marker point. In this case, the origin may be collocatedwith the center of the sensor or laser. Alternatively, mathematicalfunctions or trigonometric functions such as the tangent function may beapplied to the angles before the comparison.

In some examples, the G-PCC coder may determine whether a laser is nearthe marker point based on a comparison of vertical coordinatedifferences. For example, the G-PCC coder may compare the marker point'svertical coordinate with respect to the sensor origin (e.g., thez-coordinate of the marker point) and the vertical coordinate of thelaser intersection with the node. The G-PCC coder may obtain thevertical coordinate of the laser intersection with the node bymultiplying the tangent of the angle between the horizontal plane andthe laser direction with the distance computed by taking the Euclideandistance based on the (x,y) coordinates of the marker point(trigonometry).

In some examples, the G-PCC coder may determine the context index tocode (e.g., CABAC code) the j-th bit of the IDCM's vertical pointposition offset within the node based on the relative position of thelaser beam with respect to a vertical interval within the nodecorresponding with the j-th bit. The encoding or decoding of thevertical point position offset bits is in an order, such as frommost-significant bit (MSB) to least-significant bit (LSB). In exampleswhere the vertical point position offset bits are coded in the order ofMSB to LSB, the maximum value of the vertical point position offset and,therefore, also the MSB, is determined by the value of the vertical nodesize.

The G-PCC coder may determine that the context index is a first contextindex if the laser beam is above the mid-point of the vertical intervalcorresponding with the j-th bit. The G-PCC coder may determine that thecontext index is a second context index if the laser beam is below themid-point. The G-PCC coder may determine whether the laser beam is aboveor below the mid-point in a similar fashion as determining the laserbeam index of a laser beam that intersects with the node, e.g., bycomparing angular difference, comparing differences of tangents of anglevalues, or comparing vertical coordinate differences.

The G-PCC coder may determine distance threshold values and use thedistance threshold values to compare the distance between the laser beamand the mid-point in order to determine the context index. In someexamples, the distance threshold values may divide an interval range,corresponding with the j-th bit, within the node into subintervals. Thesubintervals may be of equal or unequal lengths. Each subinterval maycorrespond to a context index if the laser beam is within the distancerange determined by the distance subintervals. In some examples, thereare two distance thresholds determined by equal distance offsets aboveand below the mid-point. Therefore, in such examples, there may be threedistance intervals that correspond with three context indexes. The G-PCCcoder may determine whether the laser beam belongs to a subinterval in asimilar fashion as determining the laser beam index that intersects withthe node, i.e., by comparing angular difference, comparing difference oftangent of angle values, or comparing vertical coordinate difference.

The above principles, which employ sensor information, are not limitedto coding the IDCM's vertical (Z) point position offset within a node,but similar principles may also be applied to coding the X or Y pointposition offsets within a node.

In some examples, the G-PCC coder determines a context from among twocontexts based on a laser beam being positioned above or below themarker point in the interval. In this example, the mid-point is thecenter of the interval. This is illustrated in FIG. 9 . Morespecifically, FIG. 9 is a conceptual diagram of an example fordetermining a context index (e.g., Ctx=0 or Ctx=1) based on laser beampositions (solid lines) being above or below the interval mid-point 900indicated with a white circle.

As discussed above, w19088 describes a context determination method thatinvolves determination of an angular context idcmIdxAngular. Thecorresponding text in w19088 is reproduced in Table 7, below, forconvenience. Table 7 shows text for determining the context index forcoding IDCM vertical position offset bins. As shown in Table 7, theG-PCC coder evaluates five conditions to determine the context index inthe range 0 to 9. The five conditions are based on comparisons of largeinteger values. In Table 7, the conditions are indicated by <!> . . .</!> tags.

TABLE 7 A relative laser position thetaLaserDeltaVirtualIntervalrelative to the middle of the virtual interval is computed by: zLidar =((posZlidarPartial[i][j] + halfIntervalSize [j]) << 1) − 1 theta =zLidar*rInv theta32 = theta >= 0 ? theta >> 15 : −((−theta) >> 15)thetaLaserDeltaVirtualInterval = ThetaLaser − theta32 Two absoluteangular differences m and M of the laser relative to a lower and anupper z position in the virtual interval are determined: zShift = ((rInv<< EffectiveChildNodeSizeZLog2) >> 17) >> j m =abs(thetaLaserDeltaVirtualInterval − zShift) M =abs(thetaLaserDeltaVirtualInterval + zShift) Then, the angular contextis deduced from the two absolute angular differences:idcmIdxAngular[i][j] = <!>m > M</!> ? 1: 0 diff = abs(m − M) <!>if(diff >= rInv >> 15)</!> idcmIdxAngular[i][j] += 2 <!>if (diff >=rInv >> 14)</!> idcmIdxAngular[i][j] += 2 <!>if (diff >= rInv >> 13)</!>idcmIdxAngular[i][j] += 2 <!>if (diff >= rInv >> 12)</!>idcmIdxAngular[i][j] += 2

In Table 7, and elsewhere in this disclosure, zLidar indicates amidpoint in a current interval corresponding to the bit being coded.posZlidarPartial[i][j] is 0 when the most-significant bit (i.e., bit 0)is being coded, equal to halfIntervalSize when bit 1 is being coded, andthe posZlidarPartial value for each subsequent bit is equal to half ofthe posZlidarPartial value of the previous bit. halfIntervalSize[j] andzLidar are defined above. EffectiveChildNodeSizeZ Log 2 indicates a logbase-2 of a vertical node size.

A simplified two-context derivation proposed in this disclosure isspecified in Table 8, below. More specifically, Table 8 shows asimplified specification text to determine a two-context index. Thenumber of conditions is reduced to one, which is indicated with <!> . .. </!> tags. This single condition may be easy to implement because thesingle condition may be to only check the sign of an integer value.

TABLE 8 A relative laser position thetaLaserDeltaVirtualIntervalrelative to the middle of the virtual interval is computed by: zLidar =((posZlidarPartial[i][j] + halfIntervalSize [j]) << 1) − 1 theta =zLidar*rInv theta32 = theta >= 0 ? theta >> 15 : −((−theta) >> 15)thetaLaserDeltaVirtualInterval = ThetaLaser − theta32 Then, the angularcontext is deduced from the sign of thetaLaserDeltaVirtualInterval.idcmIdxAngular[i][j] = 0 <!>if (thetaLaserDeltaVirtualInterval < 0)</!>idcmIdxAngular[i][j] = 1

In some examples, the G-PCC coder determines three contexts based on thelaser beam being positioned above or below two distance thresholds, orin between the distance thresholds. In this example, the marker point isthe center of the interval. This is illustrated in FIG. 10 . Morespecifically, FIG. 10 is a conceptual diagram illustrating an exampleIDCM vertical point offset interval corresponding with j-th bit (1000)divided into three subintervals. In the example of FIG. 10 , a laserbeam 1002 is shown with a solid line and thresholds are shown withfine-dashed lines 1004A, 1004B, and a mid-point 1006 is indicated by awhite circle. Because laser beam 1002 is above line 1004A, the G-PCCcoder may select the context index ctx1.

The simplified specification text is specified in Table 9, below. Morespecifically, Table 9 below shows a simplified specification text for athree-context example. In this example, two conditions are used, whichmay be easy to implement, because the conditions only check the signs ofinteger values. The conditions are indicated with <!> . . . </!> tags.

TABLE 9 A relative laser position thetaLaserDeltaVirtualIntervalrelative to the middle of the virtual interval is computed by: zLidar =((posZlidarPartial[i][j] + halfIntervalSize [j]) << 1) − 1 theta =zLidar*rInv theta32 = theta >= 0 ? theta >> 15 : −((−theta) >> 15)thetaLaserDeltaVirtualInterval = ThetaLaser − theta32 Then, the angularcontext is deduced. zShift = ((rInv << EffectiveChildNodeSizeZLog2) >>20) >> j DeltaBottom = thetaLaserDeltaVirtualInterval + zShift DeltaTop= thetaLaserDeltaVirtualInterval − zShift idcmIdxAngular[i][j] = 0 <!>if(DeltaTop >= 0)</!> idcmIdxAngular[i][j] = 1 <!>else if (DeltaBottom <0)</!> idcmIdxAngular[i][j] = 2

In some examples, in the IDCM case, the G-PCC coder determines a contextfrom among four contexts based on a laser beam's position within similarintervals. This is illustrated in the example of FIG. 11 . FIG. 11 is aconceptual diagram illustrating an example technique for determining acontext index for coding IDCM's vertical point position offsets based ona laser beam position (solid arrow) 1102 within intervals indicated byfinely dashed lines 1104A, 1104B, 1104C. In the example of FIG. 11 , amid-point 1106 is indicated by a white circle. Because laser beam 1102is above line 1104A, the G-PCC coder may select the context index ctx2.

The proposed simplified four-context derivation is specified in Table10, below. The number of conditions is reduced to three, which areindicated with <!> . . . </!> tags. These conditions may be simple toimplement because they only check the sign of an integer value. Table 10includes specification text of a proposed simplified four-contextderivation for coding IDCM vertical point position offsets (angular modecase).

TABLE 10 ... A relative laser position thetaLaserDeltaVirtualIntervalrelative to the middle of the virtual interval is computed by: zLidar =((posZlidarPartial[i][j] + halfIntervalSize [j]) << 1) − 1 theta =zLidar*rInv theta32 = theta >= 0 ? theta >> 15 : −((−theta) >> 15)thetaLaserDeltaVirtualInterval = ThetaLaser − theta32 Then, the angularcontext is deduced based on thetaLaserDeltaVirtualInterval. zShift = ((rInv << ChildNodeSizeZLog2) >> 18 ) >> j DeltaBottom =thetaLaserDeltaVirtualInterval + zShift DeltaTop =thetaLaserDeltaVirtualInterval − zShift idcmIdxAngular[i][j] =<!>thetaLaserDeltaVirtualInterval >= 0</!> ? 0 : 1 if (<!>DeltaTop >=0</!>) idcmIdxAngular[i][j] += 2 Else if (<!>DeltaBottom < 0</!>)idcmIdxAngular[i][j] += 2 ...

FIG. 12A is a flowchart illustrating an example operation for encodingan IDCM vertical point offset in accordance with one or more techniquesof this disclosure. In the example of FIG. 12A, G-PCC encoder 200 (e.g.,arithmetic encoding unit 214 of G-PCC encoder 200 (FIG. 2 )) may encodea vertical point position offset within a node of a tree (e.g., anoctree) that represents 3-dimensional positions of points in a pointcloud represented by point cloud data (1200). As part of encoding thevertical point position offset, the G-PCC encoder 200 may determine alaser index of a laser candidate in a set of laser candidates (1202).The determined laser index indicates a laser beam that intersects thenode. G-PCC encoder 200 may determine the laser index in accordance withany of the examples provided elsewhere in this disclosure. For instance,G-PCC encoder 200 may determine the laser index as indicated in Table 2,above.

Additionally, G-PCC encoder 200 (e.g., arithmetic encoding unit 214) maydetermine a context index (idcmIdxAngular) based on whether the laserbeam is above a first distance threshold (corresponding to line 1104A),between the first distance threshold and a second distance threshold(corresponding to line 1104B), between the second distance threshold anda third distance threshold (corresponding to line 1104C), or below thethird distance threshold (1204). For instance, in the example of FIG. 11, G-PCC encoder 200 may determine a laser difference angle (e.g.,thetaLaserDeltaVirtualInterval) for an interval corresponding to the binby subtracting a tangent of the angle of the line passing through themidpoint of the node (ThetaLaser) from a tangent of an angle of theinterval (theta32). The angle of the interval may correspond to anangle, from an origin point of a set of lasers, of the interval. G-PCCencoder 200 may determine a top angle difference (e.g., DeltaTop) bysubtracting a shift value from the laser difference angle for theinterval. G-PCC encoder 200 may determine a bottom angle difference(e.g., DeltaBottom) by adding the shift value to the laser differenceangle for the interval.

G-PCC encoder 200 may perform a first comparison (e.g.,thetaLaserDeltaVirtualInterval>=0) that determines whether the laserdifference angle for the interval is greater than or equal to 0. G-PCCencoder 200 may set the context index to 0 or 1 based on whether thelaser difference angle for the interval is greater than or equal to 0(e.g., idcmIdxAngular[i][j]=thetaLaserDeltaVirtualInterval>=0? 0:1).G-PCC encoder 200 may perform a second comparison (DeltaTop>=0) thatdetermines whether the top angle difference is greater than or equal to0. The laser beam may be above the first distance threshold when the topangle difference is greater than or equal to 0. G-PCC encoder 200 mayperform a third comparison (e.g., DeltaBottom<0) that determines whetherthe bottom angle difference is less than 0. The laser beam may be belowthe third distance threshold when the bottom angle difference is lessthan 0. G-PCC encoder 200 may increment the context index by 2 based onthe top angle difference being greater than or equal to 0 (e.g., if(DeltaTop>=0) idcmIdxAngular[i][j]+=2) or based on the bottom angledifference being less than 0 (e.g., Else if (DeltaBottom<0)idcmIdxAngular[i][j]+=2).

G-PCC encoder 200 (e.g., arithmetic encoding unit 214 of G-PCC encoder200) may arithmetically encode bins of the vertical point positionoffset using a context indicated by the determined context index (1206).For example, G-PCC encoder 200 may perform CABAC encoding on bins of asyntax element indicating the vertical point position offset.

FIG. 12B is a flowchart illustrating an example operation for decodingan IDCM vertical point offset in accordance with one or more techniquesof this disclosure. In the example of FIG. 12B, G-PCC decoder 300 mayobtain a bitstream (e.g., a geometry bitstream or other type ofbitstream) that includes an arithmetically encoded syntax elementindicating a vertical point position within a node of a tree (e.g., anoctree) that represents 3-dimensional positions of points in a pointcloud represented by point cloud data (1248). For instance, G-PCCdecoder 300 may obtain the bitstream from a wired-based or wirelesscommunication channel. In some examples, G-PCC decoder 300 may obtainthe bitstream from a transitory or non-transitory computer-readablestorage medium. A processing unit, network interface, memory controller,or other type of hardware may obtain the bitstream.

G-PCC decoder 300 may decode a vertical point position offset within anode of a tree (e.g., an octree) that represents 3-dimensional positionsof points in the point cloud (1250). As part of decoding the verticalpoint position offset, G-PCC decoder 300 may determine a laser index ofa laser candidate in a set of laser candidates (1252). The determinedlaser index indicates a laser beam that intersects the node. G-PCCdecoder 300 may determine the laser index in accordance with any of theexamples provided elsewhere in this disclosure. For instance, G-PCCdecoder 300 may determine the laser index as indicated in Table 2,above.

Additionally, G-PCC decoder 300 (e.g., geometry arithmetic decoding unit302 of G-PCC decoder 300 (FIG. 3 )) may determine a context index(idcmIdxAngular) based on whether the laser beam is above a firstdistance threshold, between the first distance threshold and a seconddistance threshold, between the second distance threshold and a thirddistance threshold, or below the third distance threshold (1254). G-PCCdecoder 300 may determine the context index in the same manner as G-PCCencoder 200, as described above.

G-PCC decoder 300 (e.g., geometry arithmetic decoding unit 302 of G-PCCdecoder 300) may arithmetically decode bins of the vertical pointposition offset using a context indicated by the determined contextindex (1256). For example, G-PCC decoder 300 may perform CABAC decodingon bins of a syntax element indicating the vertical point positionoffset. G-PCC decoder 300 may perform the operation of FIG. 12B as partof reconstructing a point cloud. As part of reconstructing one or morepoints of the point cloud, G-PCC decoder 300 may determine positions ofone or more points of the point cloud based on the vertical pointposition offset. For instance, G-PCC decoder 300 may determine avertical position of a point in the reconstructed point cloud as thevertical point position offset plus the vertical position of an originpoint of the node.

As mentioned above, a number_lasers syntax element may be signaled in aparameter set, such as the geometry parameter set. The number_laserssyntax element indicates the number of lasers used for the angularcoding mode. However, in accordance with one or more techniques of thisdisclosure, the number of lasers used for the angular coding mode may besignaled (e.g., in a parameter set such as a geometry parameter set orother syntax header) as number_lasers_minusL so that the number oflasers is obtained by adding value L to the signalednumber_lasers_minusL value. Thus, in some examples, a G-PCC coder (e.g.,G-PCC encoder 200 or G-PCC decoder 300) may code a syntax element havinga first value, wherein the first value plus a second value indicates anumber of lasers, wherein the second value is a minimum number oflasers.

In some examples, the value L is equal to 1, because there should be atleast one laser for the angular mode to be useful in coding, forexample, the planar mode's plane positions or IDCM's point positionoffsets. The number_lasers_minus1 syntax element may be coded in thebitstream using variable length codes, such as k-th order exponentialGolomb codes, or fixed length codes. In some examples, the value L maybe equal to a minimum number of lasers required for the angular mode tobe useful in coding. In some examples, the number_lasers_minusL syntaxelement may be coded in the bitstream using variable length codes, suchas k-th order exponential Golomb codes, or fixed length codes.

The geometry parameter set syntax table is modified as in Table 11below, with modified text indicated with <!> . . . </!> tags. Morespecifically, Table 11 shows signaling of number_of_lasers_minus1 in thegeometry parameter set. In Table 11, <#> . . . </#> tags denote syntaxelements related to the angular mode.

TABLE 11 Descriptor geometry_parameter_set( ) { ... . . .geometry_planar_mode_flag u(1) if( geometry_planar_mode_flag ){geom_planar_mode_th_idcm ue(v) geom_planar_mode_th[ 1 ] ue(v)geom_planar_mode_th[ 2 ] ue(v) } <#> geometry_angular_mode_flag</#> u(1)<#> if( geometry_angular_mode_flag ){ </#> <#> lidar_head_position[0]</#> se(v) <#> lidar_head_position[1] </#> se(v) <#>lidar_head_position[2] </#> se(v) <!> number_lasers_minus1</!> ue(v) <#>for( i = 0; i < (number_lasers_minus1 + 1); i++ ) {</#> <#> laser_angle[i ] </#> se(v) <#> laser_correction[ i ] </#> se(v) <#> }</#> <#>planar_buffer_disabled</#> u(1) <#> se(v)implicit_qtbt_angular_max_node_min_dim_log2_to_split_z< /#> <#>implicit_qtbt_angular_max_diff_to_split_z</#> se(v) <#> } </#>neighbour_context_restriction_flag u(1)inferred_direct_coding_mode_enabled_flag u(1) ...

The semantics of the number_lasers_minus1 syntax element is given by:

number_lasers_minus1 value plus 1 specifies the number of lasers usedfor the angular coding mode. When not present, number_lasers_minus1 isinferred to −1.

In some examples of this disclosure, angular mode may be simplified byomitting inverse square root of Euclidian distance computation. Theprinciple for omitting the inverse square root of the Euclidian distancecomputation is based on the equation to compute the tangent of an angleas follows (trigonometry), as shown in FIG. 13 . FIG. 13 is a conceptualdiagram indicating corners and sides of a right triangle 1300. As shownin FIG. 13 , tangent (angle of corner A)=a/b=opposite side/adjacentside.

The more general case in three dimensions: if corner A has coordinates(X0, Y0, Z0) and corner B has coordinates (X, Y, Z), then the tangent isgiven by:tan A=(Z−Z0)/√{square root over ((X−X0)²+(Y−Y0)²)}

The inverse square root can be eliminated by squaring both sides of theequation:(tan A)²[(X−X0)²+(Y−Y0)²]=(Z−Z0)²  (Eq. 1)

In this disclosure, based on the principle above, the G-PCC coder maydetermine the index of the laser that intersects the current node, if itis eligible, and/or determine the context index for coding of the planarmode's vertical plane position using angular mode. Let Z_marker be thevertical coordinate of the marker point with respect to the origin andlet Tan_laser be the tangent of the angle between the candidate laserbeam's direction and the horizontal plane through the origin. Bothvalues may be positive or negative: Z_marker_sign and Tan_laser_sign(assuming values +1 or −1). The G-PCC coder may determining theintersecting laser index by computing the square of Z_marker (Z2_marker)and the square of Tan_laser multiplied with the square of the Euclidiandistance (Eq. 1) which determines the square of the laser's verticalcoordinate (Z2_laser). The smallest (absolute value) delta value betweenthe two quantities Z_marker_sign*Z2_marker and Tan_laser_sign*Z2_laserdetermines the index of the laser that intersects the node. Optionally,an approximate laser correction offset Z_correction_sign*Z2_correctionmay be included in the delta computation. The G-PCC coder may determinethe context index by checking the sign of the smallest delta value above(before absolute value). A positive delta value maps to one contextindex and a negative delta value maps to the second context index.Alternatively, the delta value may be compared with subintervals (seemore details in inventions above) to determine the context index, forexample, in the case of three contexts. The G-PCC coder may determinethe index of the laser that intersects the current node, if the currentnode is eligible, and/or determine the context index for coding of IDCMvertical point position offsets using angular mode. This determinationis similar in nature as the previous one (see also previous invention).

The above principles, which employ sensor information, are not limitedto coding the planar mode's vertical (Z) plane position syntax elementwithin a node, but similar principles may also be applied to coding theplanar mode's X or Y plane position syntax elements within a node.

The planar mode's X or Y plane position modes may be chosen by G-PCCencoder 200 if they are more appropriate to code the point distributionwithin the node. It is also understood that the above principles, whichemploy sensor information, are not limited to coding the IDCM's vertical(Z) point position offset within a node, but that similar principles mayalso be applied to coding the X or Y point position offsets within anode.

Building upon the text of w19088 for determining the laser index of alaser that intersects the current node as specified above, thespecification text changes of this example are provided in Table 12,below. More specifically, Table 12 below shows example specificationtext determining a laser index through the current node without inversesquare root of distance computations.

TABLE 12 ... Otherwise, if the angular eligibilityangular_eligible[Child] is equal to 1, the following applies as acontinuation of the process described in section 8.2.5.1 [1].  midNodeX= (1 << (ChildNodeSizeXLog2) >> 1)  midNodeY = (1 <<(ChildNodeSizeXLog2) >> 1)  midNodeZ = (1 << (ChildNodeSizeZLog2) >> 1) xLidar = xNchild − lidar_head_position[0] + midNodeX  yLidar = yNchild− lidar_head_position[1] + midNodeY  zLidar = zNchild −lidar_head_position[2] + midNodeZ where (xNchild, yNchild, zNchild)specifies the position of the geometry octree child node Child in thecurrent slice. The squared and scaled Euclidian distance is given by: r2= (xLidar*xLidar + yLidar*yLidar) >> 3 (note: right-shift scaling >>Nmay be required to reduce bitdepth of result) The square of the verticalcoordinate to the midNode is given by: z2 = zLidar * zLidar The angulareligibility and the associated laser to the child node are determined asfollows, based on the parent node Parent of the child node: laserIndex[Child] = UNKNOWN_LASER idcm4angular[Child] = 0 if (laserIndex [Parent]== UNKNOWN_LASER || deltaAngleR <= (midNodeZ<< (26 + 2))) { minDelta = 1<< (18 + 7) (note: minimum is determined below, so this set to maximum)for (j = 0; j < number_lasers; j++) { laser_angle2 = (((laser_angle[j] *laser_angle[j]) >> 3) * r2) >> 30) (note: laser_angle array containstangents of angles; scaling to reduce result bitdepth) laser_correction2= laser_correction[j] * laser_correction [j] delta = (+/− laser_angle2+/− laser_correction2 +/− z2) (note: +/− depending on signs oflaser_angle[j], laser_correction[j], zLidar before squaring to obtaindelta value; including laser_correction2 is optional and approximate)deltaAbs = abs(delta) deltaSign = sign(delta) if (deltaAbs < minDelta) {minDelta = deltaAbs laserIndex [Child] = j signForCtx = deltaSign } } }else idcm4angular[Child] = 1 ...

The G-PCC coder may determine the context index (contextAngular) of thelaser crossing the node above or below the node mid-point as follows inTable 13.

TABLE 13 ... The angular context is deduced from the sign of signForCtx.contextAngular[Child] = 0 if (signForCtx < 0) contextAngular[Child] = 1...

Signaling of Laser Angle and Laser Offset

For every laser, the corresponding laser angle, and laser offset (laserposition relative to the head position) are signaled (e.g., as indicatedwith text enclosed in <!> . . . </!> tags), as indicated in Table 14below.

TABLE 14 Descriptor geometry_parameter_set( ) { ... . . .geometry_planar_mode_flag u(1) if( geometry_planar_mode_flag ){geom_planar_mode_th_idcm ue(v) geom_planar_mode_th[ 1 ] ue(v)geom_planar_mode_th[ 2 ] ue(v) } geometry_angular_mode_flag u(1) if(geometry_angular_mode_flag ){ lidar_head_position[0] se(v)lidar_head_position[1] se(v) lidar_head_position[2] se(v) number_lasersue(v) <!> for( i = 0; i < number_lasers; i++ ) {</!> <!> laser_angle[ i]</!> se(v) <!> laser_correction[ i ]</!> se(v) <!> }</!>planar_buffer_disabled u(1)implicit_qtbt_angular_max_node_min_dim_log2_to_split_z se(v)implicit_qtbt_angular_max_diff_to_split_z se(v) }neighbour_context_restriction_flag u(1)inferred_direct_coding_mode_enabled_flag u(1) ...

Thus, in some examples, a device (e.g., G-PCC encoder 200 or G-PCCdecoder 300) may signal, for each laser of the set of laser candidates,a corresponding laser angle and a corresponding laser offset.

Although in the syntax description above, it is described that laserangle and laser correction are both coded with se(v) (i.e., signedinteger 0-th order Exp-Golomb coding with left bit first), the softwaredescription is different compared to the syntax description. In general,laser angle and laser corrections are represented in floating pointformat in the encoder input configuration, and the values are scaled toan integer and an offset is added to convert the integer to a positiveinteger. The positive integer may be signaled, e.g., as part of thebitstream, as follows:

for (auto val : params.encoder.lasersTheta) { int one = 1 << 18;params.encoder.gps.geom_angular_theta_laser.push_back(round(val * one));} for (auto val : params.encoder.lasersZ) { int one = 1 << 3;params.encoder.gps.geom_angular_z_laser.push_back( round(val *params.encoder.sps.seq_source_geom_scale_factor * one)); } for (int i =0; i < gps.geom_angular_num_lidar_lasers( ); i++) {bs.writeUe(gps.geom_angular_theta_laser[i] + 1048576);bs.writeUe(gps.geom_angular_z_laser[i] + 1048576); }

The laser angles may be arranged in a sorted format, e.g., the anglesare monotonically increasing or decreasing with the array index. If notarranged in this format, a preprocessing of the input can be possible tosort the angles prior to coding. It is observed that the laser anglesare very similar to each other. In this scenario, the angles of arrayindex i can be predicted from angle of index i−1, and only thedifference can be signaled, i.e., delta coding can be applied.

It is observed that the angle of a particular laser is very similar toits neighbor laser(s). In this scenario, the angle of the i-th laser canbe predicted from an angle of the (i−1)-th laser, and only thedifference can be signaled, i.e., delta coding can be applied with se(v)coding.

The similar delta coding can also be applied for the laser correction,as shown in Table 15, below.

TABLE 15 Descriptor geometry_parameter_set( ) { ... . . .geometry_planar_mode_flag u(1) if( geometry_planar_mode_flag ){geom_planar_mode_th_idcm ue(v) geom_planar_mode_th[ 1 ] ue(v)geom_planar_mode_th[ 2 ] ue(v) } geometry_angular_mode_flag u(1) if(geometry_angular_mode_flag ){ lidar_head_position[0] se(v)lidar_head_position[1] se(v) lidar_head_position[2] se(v) number_lasersue(v) <!> for( i = 0; i < number_lasers; i++ ) {</!> <!>laser_angle_delta[ i ]</!> se(v) <!> laser_correction_delta[ i ]</!>se(v) <!> }</!> planar_buffer_disabled u(1)implicit_qtbt_angular_max_node_min_dim_log2_to_split_z se(v)implicit_qtbt_angular_max_diff_to_split_z se(v) }neighbour_context_restriction_flag u(1)inferred_direct_coding_mode_enabled_flag u(1) ...

The laser angle[i] and laser_correction[i] can be derived respectivelyfrom laser_angle_delta[i] and laser_correction_delta[i], at G-PCCdecoder 300, as follows:

pred_angle = 0 ; pred_correction = 0 ; For(i=0;i<number_lasers;i++){If(i>0){  pred_angle = laser_angle[i −1];  pred_correction =laser_correction[i −1]; } laser_angle[i] = laser_angle_delta[i] +pred_angle ; laser_correction[i] = laser_correction_delta[i] +pred_correction ; }

In some examples, laser_angle_delta[i] (except for laser_angle_delta[0])can be coded as an unsigned integer if the laser angles are sorted(monotonically increasing and decreasing), as the deltas would be eitherall positive or all negative.

So, for laser_angle_delta [0], se(v) coding (i.e., signed integer 0-thorder Exp-Golomb coding with left bit first) is used, and for otherlaser_angle_delta[i] (i>0), ue(v) coding is used. laser_offset_deltasare coded with se(v), e.g., as shown in Table 16 below.

TABLE 16 Descriptor geometry_parameter_set( ) { ... . . .geometry_planar_mode_flag u(1) if( geometry_planar_mode_flag ){geom_planar_mode_th_idcm ue(v) geom_planar_mode_th[ 1 ] ue(v)geom_planar_mode_th[ 2 ] ue(v) } geometry_angular_mode_flag u(1) if(geometry_angular_mode_flag ){ lidar_head_position[0] se(v)lidar_head_position[1] se(v) lidar_head_position[2] se(v) number_lasersue(v) <!>for(i=0; i <number_lasers; i++ ) {</!> <!> if(i==0) </!><!> laser_angle_delta[ i ]</!> se(v) <!> else</!> <!> laser_angle_delta[i ]</!> ue(v) <!> laser_correction_delta[ i ]</!> se(v) <!>}</!>planar_buffer_disabled u(1)implicit_qtbt_angular_max_node_min_dim_log2_to_split_z se(v)implicit_qtbt_angular_max_diff_to_split_z se(v) }neighbour_context_restriction_flag u(1)inferred_direct_coding_mode_enabled_flag u(1) ...

In another example, laser_angle_delta[i] and laser_correction_delta[i]can be coded with Exp-Golomb code with order k. k can be self-adaptive(based on the magnitude of delta values), fixed and encoderconfigurable, or fixed and predetermined. In another example, deltacoding may only be applicable to laser angles but not laser corrections.

FIG. 14 is a conceptual diagram illustrating an example range-findingsystem 1400 that may be used with one or more techniques of thisdisclosure. In the example of FIG. 14 , range-finding system 1400includes an illuminator 1402 and a sensor 1404. Illuminator 1402 mayemit light 1406. In some examples, illuminator 1402 may emit light 1406as one or more laser beams. Light 1406 may be in one or morewavelengths, such as an infrared wavelength or a visible lightwavelength. In other examples, light 1406 is not coherent, laser light.When light 1406 encounters an object, such as object 1408, light 1406creates returning light 1410. Returning light 1410 may includebackscattered and/or reflected light. Returning light 1410 may passthrough a lens 1411 that directs returning light 1410 to create an image1412 of object 1408 on sensor 1404. Sensor 1404 generates signals 1414based on image 1412. Image 1412 may comprise a set of points (e.g., asrepresented by dots in image 1412 of FIG. 14 ).

In some examples, illuminator 1402 and sensor 1404 may be mounted on aspinning structure so that illuminator 1402 and sensor 1404 capture a360-degree view of an environment. In other examples, range-findingsystem 1400 may include one or more optical components (e.g., mirrors,collimators, diffraction gratings, etc.) that enable illuminator 1402and sensor 1404 to detect ranges of objects within a specific range(e.g., up to 360-degrees). Although the example of FIG. 14 only shows asingle illuminator 1402 and sensor 1404, range-finding system 1400 mayinclude multiple sets of illuminators and sensors.

In some examples, illuminator 1402 generates a structured light pattern.In such examples, range-finding system 1400 may include multiple sensors1404 upon which respective images of the structured light pattern areformed. Range-finding system 1400 may use disparities between the imagesof the structured light pattern to determine a distance to an object1408 from which the structured light pattern backscatters. Structuredlight-based range-finding systems may have a high level of accuracy(e.g., accuracy in the sub-millimeter range), when object 1408 isrelatively close to sensor 1404 (e.g., 0.2 meters to 2 meters). Thishigh level of accuracy may be useful in facial recognition applications,such as unlocking mobile devices (e.g., mobile phones, tablet computers,etc.) and for security applications.

In some examples, range-finding system 1400 is a time of flight(ToF)-based system. In some examples where range-finding system 1400 isa ToF-based system, illuminator 1402 generates pulses of light. In otherwords, illuminator 1402 may modulate the amplitude of emitted light1406. In such examples, sensor 1404 detects returning light 1410 fromthe pulses of light 1406 generated by illuminator 1402. Range-findingsystem 1400 may then determine a distance to object 1408 from whichlight 1406 backscatters based on a delay between when light 1406 wasemitted and detected and the known speed of light in air). In someexamples, rather than (or in addition to) modulating the amplitude ofthe emitted light 1404, illuminator 1402 may modulate the phase of theemitted light 1404. In such examples, sensor 1404 may detect the phaseof returning light 1410 from object 1408 and determine distances topoints on object 1408 using the speed of light and based on timedifferences between when illuminator 1402 generated light 1406 at aspecific phase and when sensor 1404 detected returning light 1410 at thespecific phase.

In other examples, a point cloud may be generated without usingilluminator 1402. For instance, in some examples, sensors 1404 ofrange-finding system 1400 may include two or more optical cameras. Insuch examples, range-finding system 1400 may use the optical cameras tocapture stereo images of the environment, including object 1408.Range-finding system 1400 may include a point cloud generator 1420 thatmay calculate the disparities between locations in the stereo images.Range-finding system 1400 may then use the disparities to determinedistances to the locations shown in the stereo images. From thesedistances, point cloud generator 1420 may generate a point cloud.

Sensors 1404 may also detect other attributes of object 1408, such ascolor and reflectance information. In the example of FIG. 14 , a pointcloud generator 1416 may generate a point cloud based on signals 1418generated by sensor 1404. Range-finding system 1400 and/or point cloudgenerator 1416 may form part of data source 104 (FIG. 1 ). Hence, apoint cloud generated by range-finding system 1400 may be encoded and/ordecoded according to any of the techniques of this disclosure.

FIG. 15 is a conceptual diagram illustrating an example vehicle-basedscenario in which one or more techniques of this disclosure may be used.In the example of FIG. 15 , a vehicle 1500 includes a range-findingsystem 1502. Range-finding system 1502 may be implemented in the mannerdiscussed with respect to FIG. 14 . Although not shown in the example ofFIG. 15 , vehicle 1500 may also include a data source, such as datasource 104 (FIG. 1 ), and a G-PCC encoder, such as G-PCC encoder 200(FIG. 1 ). In the example of FIG. 15 , range-finding system 1502 emitslaser beams 1504 that reflect off pedestrians 1506 or other objects in aroadway. The data source of vehicle 1500 may generate a point cloudbased on signals generated by range-finding system 1502. The G-PCCencoder of vehicle 1500 may encode the point cloud to generatebitstreams 1508, such as geometry bitstream 203 (FIG. 2 ) and attributebitstream 205 (FIG. 2 ). Bitstreams 1508 may include many fewer bitsthan the unencoded point cloud obtained by the G-PCC encoder.

An output interface of vehicle 1500 (e.g., output interface 108 (FIG. 1) may transmit bitstreams 1508 to one or more other devices. Bitstreams1508 may include many fewer bits than the unencoded point cloud obtainedby the G-PCC encoder. Thus, vehicle 1500 may be able to transmitbitstreams 1508 to other devices more quickly than the unencoded pointcloud data. Additionally, bitstreams 1508 may require less data storagecapacity. The techniques of this disclosure may further reduce thenumber of bits in bitstreams 1508. For instance, determining a contextindex based on an intersection of a laser beam and a node, and using acontext indicated by the context index for arithmetic coding of avertical plane position or a vertical point position offset, may furtherreduce the number of bits in bitstreams 1508.

In the example of FIG. 15 , vehicle 1500 may transmit bitstreams 1508 toanother vehicle 1510. Vehicle 1510 may include a G-PCC decoder, such asG-PCC decoder 300 (FIG. 1 ). The G-PCC decoder of vehicle 1510 maydecode bitstreams 1508 to reconstruct the point cloud. Vehicle 1510 mayuse the reconstructed point cloud for various purposes. For instance,vehicle 1510 may determine based on the reconstructed point cloud thatpedestrians 1506 are in the roadway ahead of vehicle 1500 and thereforestart slowing down, e.g., even before a driver of vehicle 1510 realizesthat pedestrians 1506 are in the roadway. Thus, in some examples,vehicle 1510 may perform an autonomous navigation operation based on thereconstructed point cloud.

Additionally or alternatively, vehicle 1500 may transmit bitstreams 1508to a server system 1512. Server system 1512 may use bitstreams 1508 forvarious purposes. For example, server system 1512 may store bitstreams1508 for subsequent reconstruction of the point clouds. In this example,server system 1512 may use the point clouds along with other data (e.g.,vehicle telemetry data generated by vehicle 1500) to train an autonomousdriving system. In other example, server system 1512 may storebitstreams 1508 for subsequent reconstruction for forensic crashinvestigations (e.g., if vehicle 1500 collides with pedestrians 1506).

FIG. 16 is a conceptual diagram illustrating an example extended realitysystem in which one or more techniques of this disclosure may be used.Extended reality (XR) is a term used to cover a range of technologiesthat includes augmented reality (AR), mixed reality (MR), and virtualreality (VR). In the example of FIG. 16 , a user 1600 is located in afirst location 1602. User 1600 wears an XR headset 1604. As analternative to XR headset 1604, user 1600 may use a mobile device (e.g.,mobile phone, tablet computer, etc.). XR headset 1604 includes a depthdetection sensor, such as a range-finding system, that detects positionsof points on objects 1606 at location 1602. A data source of XR headset1604 may use the signals generated by the depth detection sensor togenerate a point cloud representation of objects 1606 at location 1602.XR headset 1604 may include a G-PCC encoder (e.g., G-PCC encoder 200 ofFIG. 1 ) that is configured to encode the point cloud to generatebitstreams 1608.

XR headset 1604 may transmit bitstreams 1608 (e.g., via a network suchas the Internet) to an XR headset 1610 worn by a user 1612 at a secondlocation 1614. XR headset 1610 may decode bitstreams 1608 to reconstructthe point cloud. XR headset 1610 may use the point cloud to generate anXR visualization (e.g., an AR, MR, VR visualization) representingobjects 1606 at location 1602. Thus, in some examples, such as when XRheadset 1610 generates an VR visualization, user 1612 may have a 3Dimmersive experience of location 1602. In some examples, XR headset 1610may determine a position of a virtual object based on the reconstructedpoint cloud. For instance, XR headset 1610 may determine, based on thereconstructed point cloud, that an environment (e.g., location 1602)includes a flat surface and then determine that a virtual object (e.g.,a cartoon character) is to be positioned on the flat surface. XR headset1610 may generate an XR visualization in which the virtual object is atthe determined position. For instance, XR headset 1610 may show thecartoon character sitting on the flat surface.

The techniques of this disclosure may further reduce the number of bitsin bitstreams 1608. For instance, determining a context index based onan intersection of a laser beam and a node, and using a contextindicated by the context index for arithmetic coding of a vertical planeposition or a vertical point position offset, may further reduce thenumber of bits in bitstreams 1608.

FIG. 17 is a conceptual diagram illustrating an example mobile devicesystem in which one or more techniques of this disclosure may be used.In the example of FIG. 17 , a mobile device 1700, such as a mobile phoneor tablet computer, includes a range-finding system, such as a LIDARsystem, that detects positions of points on objects 1702 in anenvironment of mobile device 1700. A data source of mobile device 1700may use the signals generated by the depth detection sensor to generatea point cloud representation of objects 1702. Mobile device 1700 mayinclude a G-PCC encoder (e.g., G-PCC encoder 200 of FIG. 1 ) that isconfigured to encode the point cloud to generate bitstreams 1704. In theexample of FIG. 17 , mobile device 1700 may transmit bitstreams to aremote device 1706, such as a server system or other mobile device.Remote device 1706 may decode bitstreams 1704 to reconstruct the pointcloud. Remote device 1706 may use the point cloud for various purposes.For example, remote device 1706 may use the point cloud to generate amap of environment of mobile device 1700. For instance, remote device1706 may generate a map of an interior of a building based on thereconstructed point cloud. In another example, remote device 1706 maygenerate imagery (e.g., computer graphics) based on the point cloud. Forinstance, remote device 1706 may use points of the point cloud asvertices of polygons and use color attributes of the points as the basisfor shading the polygons. In some examples, remote device 1706 may usethe reconstructed point cloud for facial recognition or other securityapplications.

The techniques of this disclosure may further reduce the number of bitsin bitstreams 1704. For instance, determining a context index based onan intersection of a laser beam and a node, and using a contextindicated by the context index for arithmetic coding of a vertical planeposition or a vertical point position offset, may further reduce thenumber of bits in bitstreams 1704.

Examples in the various aspects of this disclosure may be usedindividually or in any combination.

The following is a non-limiting list of aspects that may be inaccordance with one or more techniques of this disclosure.

Aspect 1A: A method of processing a point cloud includes coding avertical plane position of a planar mode in a node of an octree thatrepresents 3-dimensional positions of points in the point cloud, whereincoding the vertical plane position of the planar mode comprises:determining a laser index of a laser candidate in a set of lasercandidates, wherein the determined laser index indicates a position of alaser beam that intersects the node; determining a context index basedon an intersection of the laser beam and the node; and arithmeticallycoding the vertical plane position of the planar mode using a contextindicated by the determined context index.

Aspect 2A: The method of aspect 1A, wherein determining the contextindex comprises determining the context index based on whether the laserbeam is positioned above or below a marker point, wherein the markerpoint is a center of the node.

Aspect 3A: The method of aspect 1A, wherein determining the contextindex comprises determining the context index based on whether the laserbeam is positioned above a first distance threshold, below a seconddistance threshold, or between the first and second distance thresholds.

Aspect 4A: The method of aspect 1A, wherein determining the contextindex comprises determining the context index based on whether the laserbeam is positioned above a first distance threshold, between the firstdistance threshold and a second distance threshold, between the seconddistance threshold and a third distance threshold, or below the thirddistance threshold.

Aspect 5A: A method of processing a point cloud includes coding avertical point position offset within a node of an octree thatrepresents 3-dimensional positions of points in the point cloud, whereincoding the vertical point position offset comprises: determining a laserindex of a laser candidate in a set of laser candidates, wherein thedetermined laser index indicates a position of a laser beam thatintersects the node; determining a context index based on anintersection of the laser beam and the node; and arithmetically codingbins of the vertical point position offset using a context indicated bythe determined context index.

Aspect 6A: The method of aspect 5A, wherein determining the contextindex comprises determining the context index based on whether the laserbeam is positioned above or below a marker point, wherein the markerpoint is a center of the node.

Aspect 7A: The method of aspect 5A, wherein determining the contextindex comprises determining the context index based on whether the laserbeam is positioned above a first distance threshold, below a seconddistance threshold, or between the first and second distance thresholds.

Aspect 8A: The method of aspect 5A, wherein determining the contextindex comprises determining the context index based on whether the laserbeam is positioned above a first distance threshold, between the firstdistance threshold and a second distance threshold, between the seconddistance threshold and a third distance threshold, or below the thirddistance threshold.

Aspect 9A: A method of processing a point cloud includes coding a syntaxelement having a first value, wherein the first value plus a secondvalue indicates a number of lasers, wherein the second value is aminimum number of lasers.

Aspect 10A: The method of aspect 9A, wherein the syntax element is afirst syntax element, the method further includes determining a laserindex of a laser candidate in a set of laser candidates, wherein the setof laser candidates has the number of lasers, and the determined laserindex indicates a position of a laser beam that intersects a node of anoctree that represents 3-dimensional positions of points in the pointcloud; determining a context index based on an intersection of the laserbeam and the node; and arithmetically coding bins of a second syntaxelement using a context indicated by the determined context index.

Aspect 11A: A method of processing a point cloud includes determining alaser index of a laser candidate in a set of laser candidates, whereinthe determined laser index indicates a position of a laser beam thatintersects a node of an octree that represents positions of3-dimensional positions of points in the point cloud; determining acontext index based on an intersection of the laser beam and the nodeusing an angular mode; and arithmetically coding bins of a second syntaxelement using a context indicated by the determined context index.

Aspect 12A: A method of processing a point cloud includes signaling, foreach laser of the set of laser candidates, a corresponding laser angleand a corresponding laser offset.

Aspect 13A: The method of aspect 12A, further comprising the methods ofany of aspects 1A-11A.

Aspect 14A: The method of any of aspects 1A-13A, further comprisinggenerating the point cloud.

Aspect 15A: A device for processing a point cloud, the device comprisingone or more means for performing the method of any of aspects 1A-14A.

Aspect 16A: The device of aspect 15A, wherein the one or more meanscomprise one or more processors implemented in circuitry.

Aspect 17A: The device of any of aspects 15A or 16A, further comprisinga memory to store the data representing the point cloud.

Aspect 18A: The device of any of aspects 15A-17A, wherein the devicecomprises a decoder.

Aspect 19A: The device of any of aspects 15A-18A, wherein the devicecomprises an encoder.

Aspect 20A: The device of any of aspects 15A-19A, further comprising adevice to generate the point cloud.

Aspect 21A: The device of any of aspects 15A-20A, further comprising adisplay to present imagery based on the point cloud.

Aspect 22A: A computer-readable storage medium having stored thereoninstructions that, when executed, cause one or more processors toperform the method of any of aspects 1A-14A.

Aspect 1B: A device for decoding point cloud data includes a memory tostore the data representing the point cloud data; and one or moreprocessors coupled to the memory and implemented in circuitry, the oneor more processors configured to: obtain a bitstream that includes anarithmetically encoded syntax element indicating a vertical planeposition of a planar mode of a node of a tree that represents3-dimensional positions of points in a point cloud represented by thepoint cloud data; and decode the vertical plane position of the planarmode in the node, wherein the one or more processors are configured suchthat, as part of decoding the vertical plane position of the planarmode, the one or more processors: determine a laser index of a lasercandidate in a set of laser candidates, wherein the determined laserindex indicates a laser beam that intersects the node; determine acontext index based on whether the laser beam is above a first distancethreshold, between the first distance threshold and a second distancethreshold, between the second distance threshold and a third distancethreshold, or below the third distance threshold; and arithmeticallydecode the vertical plane position of the planar mode using a contextindicated by the determined context index.

Aspect 2B: The device of aspect 1B, wherein the one or more processorsare configured to, as part of determining the context index, determine alaser difference angle by subtracting a tangent of an angle of a linepassing through a center of the node from a tangent of an angle of thelaser beam; determine a top angle difference by subtracting a shiftvalue from the laser difference angle; and determine a bottom angledifference by adding the shift value to the laser difference angle.

Aspect 3B: The device of aspect 2B, wherein the one or more processorsare configured to, as part of determining the context index: perform afirst comparison that determines whether the laser difference angle isgreater than or equal to 0; set the context index to 0 or 1 based onwhether the laser difference angle is greater than or equal to 0;perform a second comparison that determines whether the top angledifference is greater than or equal to 0, wherein the laser beam isabove the first distance threshold when the top angle difference isgreater than or equal to 0; perform a third comparison that determineswhether the bottom angle difference is less than 0, wherein the laserbeam is below the third distance threshold when the bottom angledifference is less than 0; and increment the context index by 2 based onthe top angle difference being greater than or equal to 0 or based onthe bottom angle difference being less than 0.

Aspect 4B: The device of any of aspects 1B through 3B, wherein the oneor more processors are further configured to reconstruct the pointcloud, and wherein the one or more processors are configured to, as partof reconstructing the point cloud, determine positions of one or morepoints of the point cloud based on the vertical plane position.

Aspect 5B: The device of any of aspects 1B through 4B, wherein the oneor more processors are further configured to generate a map of aninterior of a building based on the reconstructed point cloud.

Aspect 6B: The device of any of aspects 1B through 5B, wherein the oneor more processors are further configured to perform an autonomousnavigation operation based on the reconstructed point cloud.

Aspect 7B: The device of any of aspects 1B through 6B, wherein the oneor more processors are further configured to generate computer graphicsbased on the reconstructed point cloud.

Aspect 8B: The device of aspect 7B, wherein the one or more processorsare configured to: determine a position of a virtual object based on thereconstructed point cloud; and generate an extended reality (XR)visualization in which the virtual object is at the determined position.

Aspect 9B: The device of any of aspects 1B through 8B, wherein thedevice is one of a mobile phone or tablet computer.

Aspect 10B: The device of any of aspects 1B through 9B, wherein thedevice is a vehicle.

Aspect 11B: The device of any of aspects 1B through 10B, wherein thedevice is an extended reality device.

Aspect 12B: The device of any of aspects 1B through 11B, furthercomprising a display to present imagery based on the point cloud.

Aspect 13B: A device for encoding point cloud data includes a memory tostore the point cloud data; and one or more processors coupled to thememory and implemented in circuitry, the one or more processorsconfigured to encode a point cloud represented by the point cloud data,wherein the one or more processors are configured to, as part ofencoding the point cloud: encode a vertical plane position of a planarmode in a node of a tree that represents 3-dimensional positions ofpoints in the point cloud, wherein the one or more processors areconfigured such that, as part of encoding the vertical plane position ofthe planar mode, the one or more processors: determine a laser index ofa laser candidate in a set of laser candidates, wherein the determinedlaser index indicates a laser beam that intersects the node; determine acontext index based on whether the laser beam is above a first distancethreshold, between the first distance threshold and a second distancethreshold, between the second distance threshold and a third distancethreshold, or below the third distance threshold; and arithmeticallyencode the vertical plane position of the planar mode using a contextindicated by the determined context index.

Aspect 14B: The device of aspect 13B, wherein the one or more processorsare configured to, as part of determining the context index: determine alaser difference angle by subtracting a tangent of an angle of a linepassing through a center of the node from a tangent of an angle of thelaser beam; determine a top angle difference by subtracting a shiftvalue from the laser difference angle; and determine a bottom angledifference by adding the shift value to the laser difference angle.

Aspect 15B: The device of aspect 14B, wherein the one or more processorsare configured to, as part of determining the context index: perform afirst comparison that determines whether the laser difference angle isgreater than or equal to 0; set the context index to 0 or 1 based onwhether the laser difference angle is greater than or equal to 0;perform a second comparison that determines whether the top angledifference is greater than or equal to 0, wherein the laser beam isabove the first distance threshold when the top angle difference isgreater than or equal to 0; perform a third comparison that determineswhether the bottom angle difference is less than 0, wherein the laserbeam is below the third distance threshold when the bottom angledifference is less than 0; and increment the context index by 2 based onthe top angle difference being greater than or equal to 0 or based onthe bottom angle difference being less than 0.

Aspect 16B: The device of any of aspects 13B through 15B, wherein theone or more processors are further configured to generate the pointcloud.

Aspect 17B: The device of aspect 16B, wherein the one or more processorsare configured to, as part of generating the point cloud, generate thepoint cloud based on signals from a LIDAR apparatus.

Aspect 18B: The device of any of aspects 13B through 17B, wherein thedevice is one of a mobile phone or tablet computer.

Aspect 19B: The device of any of aspects 13B through 18B, wherein thedevice is a vehicle.

Aspect 20B: The device of any of aspects 13B through 19B, wherein thedevice is an extended reality device.

Aspect 21B: The device of any of aspects 13B through 20B, wherein thedevice comprises an interface configured to transmit the point clouddata.

Aspect 22B: A method of decoding point cloud data includes obtaining abitstream that includes an arithmetically encoded syntax elementindicating a vertical plane position of a planar mode of a node of atree that represents 3-dimensional positions of points in a point cloudrepresented by the point cloud data; and decoding the vertical planeposition of the planar mode in the node, wherein decoding the verticalplane position of the planar mode comprises: determining a laser indexof a laser candidate in a set of laser candidates, wherein thedetermined laser index indicates a laser beam that intersects the node;determining a context index based on whether the laser beam is above afirst distance threshold, between the first distance threshold and asecond distance threshold, between the second distance threshold and athird distance threshold, or below the third distance threshold; andarithmetically decoding the vertical plane position of the planar modeusing a context indicated by the determined context index.

Aspect 23B: The method of aspect 22B, wherein determining the contextindex comprises: determining a laser difference angle by subtracting atangent of an angle of a line passing through a center of the node froma tangent of an angle of the laser beam; determining a top angledifference by subtracting a shift value from the laser difference angle;and determining a bottom angle difference by adding the shift value tothe laser difference angle.

Aspect 24B: The method of aspect 23B, wherein determining the contextindex comprises: performing a first comparison that determines whetherthe laser difference angle is greater than or equal to 0; setting thecontext index to 0 or 1 based on whether the laser difference angle isgreater than or equal to 0; performing a second comparison thatdetermines whether the top angle difference is greater than or equal to0, wherein the laser beam is above the first distance threshold when thetop angle difference is greater than or equal to 0; performing a thirdcomparison that determines whether the bottom angle difference is lessthan 0, wherein the laser beam is below the third distance thresholdwhen the bottom angle difference is less than 0; and incrementing thecontext index by 2 based on the top angle difference being greater thanor equal to 0 or based on the bottom angle difference being less than 0.

Aspect 25B: A method of encoding point cloud data includes encoding avertical plane position of a planar mode in a node of a tree thatrepresents 3-dimensional positions of points in a point cloudrepresented by the point cloud data, wherein encoding the vertical planeposition of the planar mode comprises: determining a laser index of alaser candidate in a set of laser candidates, wherein the determinedlaser index indicates a laser beam that intersects the node; determininga context index based on whether the laser beam is above a firstdistance threshold, between the first distance threshold and a seconddistance threshold, between the second distance threshold and a thirddistance threshold, or below the third distance threshold; andarithmetically encoding the vertical plane position of the planar modeusing a context indicated by the determined context index.

Aspect 26B: The method of aspect 25B, wherein determining the contextindex comprises: determining a laser difference angle by subtracting atangent of an angle of a line passing through a center of the node froma tangent of an angle of the laser beam; determining a top angledifference by subtracting a shift value from the laser difference angle;and determining a bottom angle difference by adding the shift value tothe laser difference angle.

Aspect 27B: The method of aspect 26B, wherein determining the contextindex comprises: performing a first comparison that determines whetherthe laser difference angle is greater than or equal to 0; setting thecontext index to 0 or 1 based on whether the laser difference angle isgreater than or equal to 0; performing a second comparison thatdetermines whether the top angle difference is greater than or equal to0, wherein the laser beam is above the first distance threshold when thetop angle difference is greater than or equal to 0; performing a thirdcomparison that determines whether the bottom angle difference is lessthan 0, wherein the laser beam is below the third distance thresholdwhen the bottom angle difference is less than 0; and incrementing thecontext index by 2 based on the top angle difference being greater thanor equal to 0 or based on the bottom angle difference being less than 0.

Aspect 28B: The method of any of aspects 25B through 27B, furthercomprising generating the point cloud.

Aspect 29B: A device for decoding point cloud data includes means forobtaining a bitstream that includes an arithmetically encoded syntaxelement indicating a vertical plane position of a planar mode of a nodeof a tree that represents 3-dimensional positions of points in a pointcloud represented by the point cloud data; and means for decoding thevertical plane position of the planar mode in the node, wherein themeans for decoding the vertical plane position of the planar modecomprises: means for determining a laser index of a laser candidate in aset of laser candidates, wherein the determined laser index indicates alaser beam that intersects the node; means for determining a contextindex based on whether the laser beam is above a first distancethreshold, between the first distance threshold and a second distancethreshold, between the second distance threshold and a third distancethreshold, or below the third distance threshold; and means forarithmetically decoding the vertical plane position of the planar modeusing a context indicated by the determined context index.

Aspect 30B: A device for encoding point cloud data includes means forencoding a point cloud represented by the point cloud data, wherein themeans for encoding the point cloud comprises means for encoding avertical plane position of a planar mode in a node of a tree thatrepresents 3-dimensional positions of points in the point cloud, whereinthe means for encoding the vertical plane position of the planar modecomprises: means for determining a laser index of a laser candidate in aset of laser candidates, wherein the determined laser index indicates alaser beam that intersects the node; means for determining a contextindex based on whether the laser beam is above a first distancethreshold, between the first distance threshold and a second distancethreshold, between the second distance threshold and a third distancethreshold, or below the third distance threshold; and means forarithmetically encoding the vertical plane position of the planar modeusing a context indicated by the determined context index.

Aspect 31B: A computer-readable storage medium having stored thereoninstructions that, when executed, cause one or more processors to:obtain a bitstream that includes an arithmetically encoded syntaxelement indicating a vertical plane position of a planar mode of a nodeof a tree that represents 3-dimensional positions of points in a pointcloud; and decode the vertical plane position of the planar mode in thenode, wherein the instructions that cause the one or more processors todecode the vertical plane position of the planar mode compriseinstructions that, when executed, cause the one or more processors to:determine a laser index of a laser candidate in a set of lasercandidates, wherein the determined laser index indicates a laser beamthat intersects the node; determine a context index based on whether thelaser beam is above a first distance threshold, between the firstdistance threshold and a second distance threshold, between the seconddistance threshold and a third distance threshold, or below the thirddistance threshold; and arithmetically decode the vertical planeposition of the planar mode using a context indicated by the determinedcontext index.

Aspect 32B: A computer-readable storage medium having stored thereoninstructions that, when executed, cause one or more processors to:encode a point cloud, wherein the instructions that cause the one ormore processors to encode the point cloud comprises instructions that,when executed, cause the one or more processors to encode a verticalplane position of a planar mode in a node of a tree that represents3-dimensional positions of points in the point cloud, wherein theinstructions that cause the one or more processors to encode thevertical plane position of the planar mode comprise instructions that,when executed, cause the one or more processors to: determine a laserindex of a laser candidate in a set of laser candidates, wherein thedetermined laser index indicates a laser beam that intersects the node;determine a context index based on whether the laser beam is above afirst distance threshold, between the first distance threshold and asecond distance threshold, between the second distance threshold and athird distance threshold, or below the third distance threshold; andarithmetically encode the vertical plane position of the planar modeusing a context indicated by the determined context index.

It is to be recognized that depending on the example, certain acts orevents of any of the techniques described herein can be performed in adifferent sequence, may be added, merged, or left out altogether (e.g.,not all described acts or events are necessary for the practice of thetechniques). Moreover, in certain examples, acts or events may beperformed concurrently, e.g., through multi-threaded processing,interrupt processing, or multiple processors, rather than sequentially.

In one or more examples, the functions described may be implemented inhardware, software, firmware, or any combination thereof. If implementedin software, the functions may be stored on or transmitted over as oneor more instructions or code on a computer-readable medium and executedby a hardware-based processing unit. Computer-readable media may includecomputer-readable storage media, which corresponds to a tangible mediumsuch as data storage media, or communication media including any mediumthat facilitates transfer of a computer program from one place toanother, e.g., according to a communication protocol. In this manner,computer-readable media generally may correspond to (1) tangiblecomputer-readable storage media which is non-transitory or (2) acommunication medium such as a signal or carrier wave. Data storagemedia may be any available media that can be accessed by one or morecomputers or one or more processors to retrieve instructions, codeand/or data structures for implementation of the techniques described inthis disclosure. A computer program product may include acomputer-readable medium.

By way of example, and not limitation, such computer-readable storagemedia can comprise RAM, ROM, EEPROM, CD-ROM or other optical diskstorage, magnetic disk storage, or other magnetic storage devices, flashmemory, or any other medium that can be used to store desired programcode in the form of instructions or data structures and that can beaccessed by a computer. Also, any connection is properly termed acomputer-readable medium. For example, if instructions are transmittedfrom a website, server, or other remote source using a coaxial cable,fiber optic cable, twisted pair, digital subscriber line (DSL), orwireless technologies such as infrared, radio, and microwave, then thecoaxial cable, fiber optic cable, twisted pair, DSL, or wirelesstechnologies such as infrared, radio, and microwave are included in thedefinition of medium. It should be understood, however, thatcomputer-readable storage media and data storage media do not includeconnections, carrier waves, signals, or other transitory media, but areinstead directed to non-transitory, tangible storage media. Disk anddisc, as used herein, includes compact disc (CD), laser disc, opticaldisc, digital versatile disc (DVD), floppy disk and Blu-ray disc, wheredisks usually reproduce data magnetically, while discs reproduce dataoptically with lasers. Combinations of the above should also be includedwithin the scope of computer-readable media.

Instructions may be executed by one or more processors, such as one ormore digital signal processors (DSPs), general purpose microprocessors,application specific integrated circuits (ASICs), field programmablegate arrays (FPGAs), or other equivalent integrated or discrete logiccircuitry. Accordingly, the terms “processor” and “processingcircuitry,” as used herein may refer to any of the foregoing structuresor any other structure suitable for implementation of the techniquesdescribed herein. In addition, in some aspects, the functionalitydescribed herein may be provided within dedicated hardware and/orsoftware modules configured for encoding and decoding, or incorporatedin a combined codec. Also, the techniques could be fully implemented inone or more circuits or logic elements.

The techniques of this disclosure may be implemented in a wide varietyof devices or apparatuses, including a wireless handset, an integratedcircuit (IC) or a set of ICs (e.g., a chip set). Various components,modules, or units are described in this disclosure to emphasizefunctional aspects of devices configured to perform the disclosedtechniques, but do not necessarily require realization by differenthardware units. Rather, as described above, various units may becombined in a codec hardware unit or provided by a collection ofinteroperative hardware units, including one or more processors asdescribed above, in conjunction with suitable software and/or firmware.

Various examples have been described. These and other examples arewithin the scope of the following claims.

What is claimed is:
 1. A device for decoding point cloud data, thedevice comprising: a memory to store the point cloud data; and one ormore processors coupled to the memory and implemented in circuitry, theone or more processors configured to: obtain a bitstream that includesan arithmetically encoded syntax element indicating a vertical planeposition of a planar mode in a node of a tree that represents3-dimensional positions of points in a point cloud represented by thepoint cloud data; and decode the vertical plane position of the planarmode in the node, wherein the one or more processors are configured suchthat, as part of decoding the vertical plane position of the planarmode, the one or more processors: determine a laser index of a lasercandidate in a set of laser candidates, wherein the determined laserindex indicates a laser beam that intersects the node; determine acontext index based on whether the laser beam is above a first distancethreshold, between the first distance threshold and a second distancethreshold, between the second distance threshold and a third distancethreshold, or below the third distance threshold; and arithmeticallydecode the vertical plane position of the planar mode using a contextindicated by the determined context index.
 2. The device of claim 1,wherein the one or more processors are configured to, as part ofdetermining the context index: determine a laser difference angle bysubtracting a tangent of an angle of a line passing through a center ofthe node from a tangent of an angle of the laser beam; determine a topangle difference by subtracting a shift value from the laser differenceangle; and determine a bottom angle difference by adding the shift valueto the laser difference angle.
 3. The device of claim 2, wherein the oneor more processors are configured to, as part of determining the contextindex: perform a first comparison that determines whether the laserdifference angle is greater than or equal to 0; set the context index to0 or 1 based on whether the laser difference angle is greater than orequal to 0; perform a second comparison that determines whether the topangle difference is greater than or equal to 0, wherein the laser beamis above the first distance threshold when the top angle difference isgreater than or equal to 0; perform a third comparison that determineswhether the bottom angle difference is less than 0, wherein the laserbeam is below the third distance threshold when the bottom angledifference is less than 0; and increment the context index by 2 based onthe top angle difference being greater than or equal to 0 or based onthe bottom angle difference being less than
 0. 4. The device of claim 1,wherein the one or more processors are further configured to reconstructthe point cloud, and wherein the one or more processors are configuredto, as part of reconstructing the point cloud, determine positions ofone or more points of the point cloud based on the vertical planeposition.
 5. The device of claim 4, wherein the one or more processorsare further configured to generate a map of an interior of a buildingbased on the reconstructed point cloud.
 6. The device of claim 4,wherein the one or more processors are further configured to perform anautonomous navigation operation based on the reconstructed point cloud.7. The device of claim 4, wherein the one or more processors are furtherconfigured to generate computer graphics based on the reconstructedpoint cloud.
 8. The device of claim 7, wherein the one or moreprocessors are configured to: determine a position of a virtual objectbased on the reconstructed point cloud; and generate an extended reality(XR) visualization in which the virtual object is at the determinedposition.
 9. The device of claim 1, wherein the device is one of amobile phone or tablet computer.
 10. The device of claim 1, wherein thedevice is a vehicle.
 11. The device of claim 1, wherein the device is anextended reality device.
 12. The device of claim 1, further comprising adisplay to present imagery based on the point cloud.
 13. A device forencoding point cloud data, the device comprising: a memory to store thepoint cloud data; and one or more processors coupled to the memory andimplemented in circuitry, the one or more processors configured toencode a point cloud represented by the point cloud data, wherein theone or more processors are configured to, as part of encoding the pointcloud: encode a vertical plane position of a planar mode in a node of atree that represents 3-dimensional positions of points in the pointcloud, wherein the one or more processors are configured such that, aspart of encoding the vertical plane position of the planar mode, the oneor more processors: determine a laser index of a laser candidate in aset of laser candidates, wherein the determined laser index indicates alaser beam that intersects the node; determine a context index based onwhether the laser beam is above a first distance threshold, between thefirst distance threshold and a second distance threshold, between thesecond distance threshold and a third distance threshold, or below thethird distance threshold; and arithmetically encode the vertical planeposition of the planar mode using a context indicated by the determinedcontext index.
 14. The device of claim 13, wherein the one or moreprocessors are configured to, as part of determining the context index:determine a laser difference angle by subtracting a tangent of an angleof a line passing through a center of the node from a tangent of anangle of the laser beam; determine a top angle difference by subtractinga shift value from the laser difference angle; and determine a bottomangle difference by adding the shift value to the laser differenceangle.
 15. The device of claim 14, wherein the one or more processorsare configured to, as part of determining the context index: perform afirst comparison that determines whether the laser difference angle isgreater than or equal to 0; set the context index to 0 or 1 based onwhether the laser difference angle is greater than or equal to 0;perform a second comparison that determines whether the top angledifference is greater than or equal to 0, wherein the laser beam isabove the first distance threshold when the top angle difference isgreater than or equal to 0; perform a third comparison that determineswhether the bottom angle difference is less than 0, wherein the laserbeam is below the third distance threshold when the bottom angledifference is less than 0; and increment the context index by 2 based onthe top angle difference being greater than or equal to 0 or based onthe bottom angle difference being less than
 0. 16. The device of claim13, wherein the one or more processors are further configured togenerate the point cloud.
 17. The device of claim 16, wherein the one ormore processors are configured to, as part of generating the pointcloud, generate the point cloud based on signals from a LIDAR apparatus.18. The device of claim 13, wherein the device is one of a mobile phoneor tablet computer.
 19. The device of claim 13, wherein the device is avehicle.
 20. The device of claim 13, wherein the device is an extendedreality device.
 21. The device of claim 13, wherein the device comprisesan interface configured to transmit the point cloud data.
 22. A methodof decoding point cloud data, the method comprising: obtaining abitstream that includes an arithmetically encoded syntax elementindicating a vertical plane position of a planar mode in a node of atree that represents 3-dimensional positions of points in a point cloudrepresented by the point cloud data; and decoding the vertical planeposition of the planar mode in the node, wherein decoding the verticalplane position of the planar mode comprises: determining a laser indexof a laser candidate in a set of laser candidates, wherein thedetermined laser index indicates a laser beam that intersects the node;determining a context index based on whether the laser beam is above afirst distance threshold, between the first distance threshold and asecond distance threshold, between the second distance threshold and athird distance threshold, or below the third distance threshold; andarithmetically decoding the vertical plane position of the planar modeusing a context indicated by the determined context index.
 23. Themethod of claim 22, wherein determining the context index comprises:determining a laser difference angle by subtracting a tangent of anangle of a line passing through a center of the node from a tangent ofan angle of the laser beam; determining a top angle difference bysubtracting a shift value from the laser difference angle; anddetermining a bottom angle difference by adding the shift value to thelaser difference angle.
 24. The method of claim 23, wherein determiningthe context index comprises: performing a first comparison thatdetermines whether the laser difference angle is greater than or equalto 0; setting the context index to 0 or 1 based on whether the laserdifference angle is greater than or equal to 0; performing a secondcomparison that determines whether the top angle difference is greaterthan or equal to 0, wherein the laser beam is above the first distancethreshold when the top angle difference is greater than or equal to 0;performing a third comparison that determines whether the bottom angledifference is less than 0, wherein the laser beam is below the thirddistance threshold when the bottom angle difference is less than 0; andincrementing the context index by 2 based on the top angle differencebeing greater than or equal to 0 or based on the bottom angle differencebeing less than
 0. 25. A method of encoding point cloud data, the methodcomprising: encoding a vertical plane position of a planar mode in anode of a tree that represents 3-dimensional positions of points in apoint cloud represented by the point cloud data, wherein encoding thevertical plane position of the planar mode comprises: determining alaser index of a laser candidate in a set of laser candidates, whereinthe determined laser index indicates a laser beam that intersects thenode; determining a context index based on whether the laser beam isabove a first distance threshold, between the first distance thresholdand a second distance threshold, between the second distance thresholdand a third distance threshold, or below the third distance threshold;and arithmetically encoding the vertical plane position of the planarmode using a context indicated by the determined context index.
 26. Themethod of claim 25, wherein determining the context index comprises:determining a laser difference angle by subtracting a tangent of anangle of a line passing through a center of the node from a tangent ofan angle of the laser beam; determining a top angle difference bysubtracting a shift value from the laser difference angle; anddetermining a bottom angle difference by adding the shift value to thelaser difference angle.
 27. The method of claim 26, wherein determiningthe context index comprises: performing a first comparison thatdetermines whether the laser difference angle is greater than or equalto 0; setting the context index to 0 or 1 based on whether the laserdifference angle is greater than or equal to 0; performing a secondcomparison that determines whether the top angle difference is greaterthan or equal to 0, wherein the laser beam is above the first distancethreshold when the top angle difference is greater than or equal to 0;performing a third comparison that determines whether the bottom angledifference is less than 0, wherein the laser beam is below the thirddistance threshold when the bottom angle difference is less than 0; andincrementing the context index by 2 based on the top angle differencebeing greater than or equal to 0 or based on the bottom angle differencebeing less than
 0. 28. The method of claim 25, further comprisinggenerating the point cloud.
 29. A device for decoding point cloud data,the device comprising: means for obtaining a bitstream that includes anarithmetically encoded syntax element indicating a vertical planeposition of a planar mode in a node of a tree that represents3-dimensional positions of points in a point cloud represented by thepoint cloud data; and means for decoding the vertical plane position ofthe planar mode in the node, wherein the means for decoding the verticalplane position of the planar mode comprises: means for determining alaser index of a laser candidate in a set of laser candidates, whereinthe determined laser index indicates a laser beam that intersects thenode; means for determining a context index based on whether the laserbeam is above a first distance threshold, between the first distancethreshold and a second distance threshold, between the second distancethreshold and a third distance threshold, or below the third distancethreshold; and means for arithmetically decoding the vertical planeposition of the planar mode using a context indicated by the determinedcontext index.
 30. A device for encoding point cloud data, the devicecomprising: means for encoding a point cloud represented by the pointcloud data, wherein the means for encoding the point cloud comprisesmeans for encoding a vertical plane position of a planar mode in a nodeof a tree that represents 3-dimensional positions of points in the pointcloud, wherein the means for encoding the vertical plane position of theplanar mode comprises: means for determining a laser index of a lasercandidate in a set of laser candidates, wherein the determined laserindex indicates a laser beam that intersects the node; means fordetermining a context index based on whether the laser beam is above afirst distance threshold, between the first distance threshold and asecond distance threshold, between the second distance threshold and athird distance threshold, or below the third distance threshold; andmeans for arithmetically encoding the vertical plane position of theplanar mode using a context indicated by the determined context index.31. A computer-readable storage medium having stored thereoninstructions that, when executed, cause one or more processors to:obtain a bitstream that includes an arithmetically encoded syntaxelement indicating a vertical plane position of a planar mode in a nodeof a tree that represents 3-dimensional positions of points in a pointcloud; and decode the vertical plane position of the planar mode in thenode, wherein the instructions that cause the one or more processors todecode the vertical plane position of the planar mode compriseinstructions that, when executed, cause the one or more processors to:determine a laser index of a laser candidate in a set of lasercandidates, wherein the determined laser index indicates a laser beamthat intersects the node; determine a context index based on whether thelaser beam is above a first distance threshold, between the firstdistance threshold and a second distance threshold, between the seconddistance threshold and a third distance threshold, or below the thirddistance threshold; and arithmetically decode the vertical planeposition of the planar mode using a context indicated by the determinedcontext index.
 32. A computer-readable storage medium having storedthereon instructions that, when executed, cause one or more processorsto: encode a point cloud, wherein the instructions that cause the one ormore processors to encode the point cloud comprises instructions that,when executed, cause the one or more processors to encode a verticalplane position of a planar mode in a node of a tree that represents3-dimensional positions of points in the point cloud, wherein theinstructions that cause the one or more processors to encode thevertical plane position of the planar mode comprise instructions that,when executed, cause the one or more processors to: determine a laserindex of a laser candidate in a set of laser candidates, wherein thedetermined laser index indicates a laser beam that intersects the node;determine a context index based on whether the laser beam is above afirst distance threshold, between the first distance threshold and asecond distance threshold, between the second distance threshold and athird distance threshold, or below the third distance threshold; andarithmetically encode the vertical plane position of the planar modeusing a context indicated by the determined context index.